To see the other types of publications on this topic, follow the link: Real-time objects detection.
Journal articles on the topic 'Real-time objects detection'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 journal articles for your research on the topic 'Real-time objects detection.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.
1
Prof. Vasudha Bahl and Prof. Nidhi Sengar, Akash Kumar, Dr Amita Goel. "Real-Time Object Detection Model." International Journal for Modern Trends in Science and Technology 6, no. 12 (2020): 360–64. http://dx.doi.org/10.46501/ijmtst061267.
Full textAbstract:
Object Detection is a study in the field of computer vision. An object detection model recognizes objects of the real world present either in a captured image or in real-time video where the object can belong to any class of objects namely humans, animals, objects, etc. This project is an implementation of an algorithm based on object detection called You Only Look Once (YOLO v3). The architecture of yolo model is extremely fast compared to all previous methods. Yolov3 model executes a single neural network to the given image and then divides the image into predetermined bounding boxes. These boxes are weighted by the predicted probabilities. After non max-suppression it gives the result of recognized objects together with bounding boxes. Yolo trains and directly executes object detection on full images.
2
M.S ,, Nidhishree. "Real Time Object Detection System Using Yolov." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem32036.
Full textAbstract:
Improving object detection models in computer vision is a crucial area of focus. Object detection represents a more sophisticated approach to image classification, where a neural network identifies objects within an image and delineates them using bounding boxes. YOLO (You Only Look Once) revolutionized object detection by introducing an end-to-end neural network architecture that simultaneously predicts bounding boxes and class probabilities. Unlike earlier methods that adapted classifiers for detection, YOLO's approach streamlines the process by integrating classification and localization tasks, accurately delineating objects with bounding boxes. The project mainly focuses on augmenting YOLOv4, a state-of-the-art object detection architecture, to address the limitations like small object detection, adaptive lighting handling, occlusion challenges, detecting objects at different angles, and specialized object features. Additionally, we aim to improve the model's ability to recognize specialized object features for superior classification. Furthermore, by incorporating functionalities like license plate recognition using Tesseract OCR, object counting (total and class-wise), and detection cropping, this project extends the model's applicability to real-world scenarios. These functionalities make the model valuable in areas like autonomous vehicles, traffic monitoring, and industrial automation. The augmented model's improvements aim to overcome drawbacks identified in existing research, enhancing its efficacy in various scenarios.
3
Mondal, Sudipto Kumar, Sanhita Dey, and Soumyajit Dey. "Real-time object detection comparative study." American Journal of Electronics & Communication 2, no. 2 (2021): 1–4. http://dx.doi.org/10.15864/ajec.2201.
Full textAbstract:
Object detection is a computer technology related to computer vision and image processing that deals with detecting instances of semantic objects of a certain class (such as humans, buildings, or cars) in digital images and videos. In this domain of object detection, it includes many detecting methods , such as face detection and also pedestrian detection. Object detection has applications in huge areas of computer vision, which includes image retrieval, video surveillance. Deep Neural methods in object detection using one-stage processes generally include di erent versions of YOLO and SSD. The paper which we are publishing here we are comparing some the image detection algorithms. In this project we are going to develop a system for visually impaired people for assisting them in their daily work and give them a free life.
4
SHAMILI S, FRANCIS. "YOLOV4 Based Blind Assistant System for Real-Time Object Detection." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem46943.
Full textAbstract:
Abstract – This project focuses on object detection using the YOLOv4-tiny model, a lightweight version of the YOLO (You Only Look Once) algorithm designed for real-time object detection. The model is loaded with pre-trained weights and a configuration file, making it capable of detecting various objects from a webcam feed. Once an object is detected, the system identifies the class, evaluates the confidence of the detection, and calculates the bounding box coordinates to highlight the object in the image. The system applies Non-Maximum Suppression (NMS) to remove overlapping bounding boxes and retain only the most relevant ones. In this implementation, the program captures video frames from a webcam, pre-processes them to a format suitable for the YOLO model, and passes them through the neural network to generate predictions. These predictions are then analyzed to identify the objects in the frame, and relevant details such as the object’s label and confidence score are extracted. If an object belongs to a specified class (e.g., "elephant," "bird," "horse," or "zebra"), the system triggers an HTTP request to send this information to the Blynk IoT platform for remote monitoring. The integration with Blynk IoT allows for real-time monitoring and remote alerts. By sending the detected object’s label to Blynk, the system facilitates quick action based on the objects being tracked. This setup could be used for various applications, including surveillance, automated tracking systems, and environments where real-time detection and remote reporting are essential. Additionally, the use of the YOLOv4-tiny model ensures that the object detection process is both fast and efficient, making it suitable for applications requiring low-latency responses.
5
I, Ankith. "Real Time Object Detection Using YoloReal Time Object Detection Using Yolo." International Journal for Research in Applied Science and Engineering Technology 9, no. 11 (2021): 1504–11. http://dx.doi.org/10.22214/ijraset.2021.39044.
Full textAbstract:
Abstract: Object detection is related to computer vision and involves identifying the kinds of objects that have been detected. It is challenging to detect and classify objects. Recent advances in deep learning have allowed it to detect objects more accurately. In the past, there were several methods or tools used: R-CNN, Fast-RCNN, Faster-RCNN, YOLO, SSD, etc. This research focuses on "You Only Look Once" (YOLO) as a type of Convolutional Neural Network. Results will be accurate and timely when tested. So, we analysed YOLOv3's work by using Yolo3-tiny to detect both image and video objects. Keywords: YOLO, Intersection over Union (IOU), Anchor box, Non-Max Suppression, YOLO application, limitation.
6
Karanam, Madhavi, Varun Kumar Kamani, Vikas Kuchana, Gopal Krishna Reddy Koppula, and Gautham Gongada. "Object and it’s dimension detection in real time." E3S Web of Conferences 391 (2023): 01016. http://dx.doi.org/10.1051/e3sconf/202339101016.
Full textAbstract:
Object and its dimension detection from images and videos can be very helpful for everyday use. This paper discusses the use of the system to detect an object in real time and provide its dimensions upon demand. The object dimension measurement and detection are some of the important topics of computer vision which helps in automating the manual tasks. Human beings are capable of recognizing and spotting objects in images and videos, but computers lack that ability with out prior training. To train the computer, we must use machine learning, computer vision, and object detection algorithms. This project provides the way to detect and measure an object’s dimension in real time from a webcam. To estimate the object’s dimension in real time, we have utilized the OpenCV and NumPy libraries. Computer vision provides support to computers to observe and understand. Computer vision helps the computer in understanding a 3D surrounding from a 2D image and trains the computer to perform different functions. It also helps in Human Computer Interaction effectively because it is able to differentiate the objects with surroundings and provide us with the key information.
7
Saikumar P and Divya TL. "Real-time object detection and augmentation." World Journal of Advanced Engineering Technology and Sciences 12, no. 2 (2024): 938–44. http://dx.doi.org/10.30574/wjaets.2024.12.2.0359.
Full textAbstract:
This study presents the development of a real-time object detection and augmentation system using TensorFlow and Unity it leverages TensorFlow model to identify and classify objects in real-time from a camera feed, and Unity to integrate and display corresponding 3D virtual objects in an augmented reality environment. By creating a mapping system to pair detected objects with their virtual counterparts, the system aims to enhance user interaction through dynamic and immersive AR experiences. The study addresses the challenges of real-time processing and seamless integration, demonstrating the potential of combining machine learning and augmented reality to enrich interactive applications across various domains.
8
Singh, Baljeet, Nitin Kumar, Irshad Ahmed, and Karun Yadav. "Real-Time Object Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3159–60. http://dx.doi.org/10.22214/ijraset.2022.42820.
Full textAbstract:
Abstract: The computer vision field known as real-time acquisition is large, dynamic, and complex. Local image process refers to the acquisition of one object in an image, while Objects refers to the acquisition of multiple objects in an image. In digital photos and videos, this sees semantic class objects. Tracking features, video surveilance, pedestrian detection, census, self-driving cars, face recognition, sports tracking, and many other applications used to find real-time object. Convolution Neural Networks is an in-depth study tool for OpenCV (Opensource Computer Vision), a set of basic computer-assisted programming tasks. Computer visualization, in-depth study, and convolutional neural networks are some of the words used in this paper..
9
Iustisia Natalia Simbolon, Daniel Fernandez Lumbanraja, and Kristina Tampubolon. "ANALYSIS AND IMPLEMENTATION OF YOLOV7 IN DETECTING PIN DEL IN REAL-TIME." Jurnal Teknik Informatika (Jutif) 5, no. 2 (2024): 579–87. https://doi.org/10.52436/1.jutif.2024.5.2.1286.
Full textAbstract:
Real-time object detection is the process of identifying and tracking objects instantly and directly without any delay between image input and output. Carrying out real-time detection is a challenge in detection systems because it requires speed and accuracy of detection. This research proposes the application of the YOLOv7 algorithm which allows object localization and classification in one stage. This detection is carried out in real time on two objects, namely PinDel and Students. This research focuses on applying the YOLOv7 algorithm to detect real-time use of Pin Del by students. In this research, several hyperparameters were adjusted until the optimal value was found, including epoch with a value of 300, as well as confidence threshold, and IoU threshold with a value of 0.5. The model evaluation results from hyperparameter experiments show good results, with precision of 0.946, recall of 0.959, and mAP@0.5 of 0.977. This research has succeeded in detecting Pin Del objects in real time by obtaining a detection speed of between 7 and 40 FPS, which shows a fast response in detecting objects in real time. This research has contributed to the development of real-time object detection technology and its application in Pin Del use cases by students.
10
Soppari, Dr Kavitha, D. Varun, Eedula Rithvik, and Manchala Anudeep. "Portable Object Detection in Real-Time." International Scientific Journal of Engineering and Management 04, no. 02 (2025): 1–11. https://doi.org/10.55041/isjem02269.
Full textAbstract:
Portable Object Detection in Real-Time is a computer vision- based project that enables the identification and classification of objects using a laptop's built-in camera. The system leverages deep learning techniques, specifically convolutional neural networks (CNNs) and pre-trained models such as YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector), to perform efficient and accurate object detection. The project aims to provide a lightweight and portable solution without requiring external hardware, making it accessible for various applications such as security monitoring, automated inventory management, and assistive technologies. The system processes live video feed, detects objects in real time, and displays results dynamically. This approach ensures high-speed performance while maintaining accuracy, making it suitable for real-world deployment in resource-constrained environments. Keywords: Object Detection, Real-Time Processing, Computer Vision, Deep Learning, Convolutional Neural Networks (CNNs), YOLO, SSD, Machine Learning, Laptop Camera, Portable Solution
11
Kinthali, Saketh. "Real-Time Object Detection and Distance Mapping." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 1734–40. https://doi.org/10.22214/ijraset.2025.70471.
Full textAbstract:
Abstract: This initiative involves an advanced AI-driven object detection system that employs the YOLO (You Only Look Once) deep learning framework to recognize and monitor objects in real-time from both images and videos. It comprises multiple Python scripts, including object_detection.py, real_time.py, and video_with_distance.py, which facilitate the identification of objects in photographs, live video feeds, and the estimation of their distances. The system is equipped with pre-trained YOLO model weights (yolov8m.pt, yolov8n.pt), enabling rapid and effective object recognition. Additionally, it provides sample videos (33.mp4, 34.mp4) and images (bus.jpg, output_detected.jpg) to evaluate the performance of the detection model. A COCO dataset file (coco.txt) is included, signifying that the model has been trained to identify a diverse range of common objects. Furthermore, other Python scripts (conv.py, test4.py) appear to be utilized for data conversion, testing, or enhancing the system's capabilities. This project holds significant potential for applications in real-time surveillance, autonomous vehicles, intelligent traffic management, security systems, and AI-driven automation, thereby improving the efficiency of object detection and tracking across various practical scenarios.
12
Taralathasri, Bobburi, Dammati Vidya Sri, Gadidammalla Narendra Kumar, Annam Subbarao, and Palli R. Krishna Prasad. "REAL TIME OBJECT DETECTION USING YOLO ALGORITHM." International Journal of Computer Science and Mobile Computing 10, no. 7 (2021): 61–67. http://dx.doi.org/10.47760/ijcsmc.2021.v10i07.009.
Full textAbstract:
The major and wide range applications like Driverless cars, robots, Image surveillance has become famous in the Computer vision .Computer vision is the core in all those applications which is responsible for the image detection and it became more popular worldwide. Object Detection System using Deep Learning Technique” detects objects efficiently based on YOLO algorithm and applies the algorithm on image data to detect objects.
13
Lakhotiya, Rushikesh, Mayuresh Chavan, Satwik Divate, and Soham Pande. "Image Detection and Real Time Object Detection." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 2785–90. http://dx.doi.org/10.22214/ijraset.2023.51839.
Full textAbstract:
Abstract: Object detection is a crucial task in computer vision with various practical applications, including surveillance, autonomous vehicles, and robotics. The YOLO (You Only Look Once) algorithm is a popular real-time object detection algorithm that has gained significant attention due to its high accuracy and speed. This algorithm processes the entire image at once and predicts bounding boxes and class probabilities for identified objects, making it ideal for time-sensitive applications. YOLO has evolved through various versions, with YOLOv5 being the latest and most advanced version that employs a feature pyramid network (FPN) and anchor boxes to improve its object detection accuracy. In this project, we aim to implement YOLOv5 for real-time object detection and image detection tasks. We will train the model on a suitable dataset and evaluate its performance on various benchmarks, comparing it with other advanced object detection algorithms. The project's outcome will provide a robust and efficient solution for real-time object detection that can aid quick decision-making in identifying object categories and their respective positions. It has practical applications in surveillance, automated driving, and robotics.
14
V, Annapoorani, Ananth S, and Pradeep N. "Utilizing Deep Learning to Detect Objects in Real Time." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 1116–20. http://dx.doi.org/10.22214/ijraset.2023.51703.
Full textAbstract:
Abstract: Computer vision is related to object detection. Detecting instances of objects in images and videos is made possible by object detection. It recognizes the component of Pictures rather than conventional article recognition techniques and produces an keen comprehension of pictures very much like human vision works. In this paper, We restored starts the concise presentation of profound learning and item discovery system like Convolutional Brain Network (CNN), Repetitive brain network (RNN), quicker RNN, You just look once (Consequences be damned). After that, we concentrate on the modifications to our object detection architectures that we have proposed. In images, the conventional model can identify a small object. We have a few changes to the model. The method we propose yields the correct result precisely.
15
Tanuja, Kayarga. "Multiple Object Detection and Tracking in Dynamic Environment using Real Time Video." International Journal of Trend in Scientific Research and Development 2, no. 1 (2017): 1090–99. https://doi.org/10.31142/ijtsrd7181.
Full textAbstract:
Video surveillance is an active research topic in computer vision that tries to detect, recognize and track objects over a sequence of images and it also makes an attempt to understand and describe object behavior by replacing the aging old traditional method of monitoring cameras by human operators. Object detection and tracking are important and challenging tasks in many computer vision applications such as surveillance, vehicle navigation and autonomous robot navigation. Object detection involves locating objects in the frame of a video sequence. Every tracking method requires an object detection mechanism either in every frame or when the object first appears in the video. Object tracking is the process of locating an object or multiple objects over time using a camera. The high powered computers, the availability of high quality and inexpensive video cameras and the increasing need for automated video analysis has generated a great deal of interest in object tracking algorithms. There are three key steps in video analysis, detection interesting moving objects, tracking of such objects from each and every frame to frame, and analysis of object tracks to recognize their behavior. The main reason is that they need strong requirements to achieve satisfactory working conditions, specialized and expensive hardware, complex installations and setup procedures, and supervision of qualified workers. Some works have focused on developing automatic detection and Tracking algorithms that minimizes the necessity of supervision. They typically use a moving object function that evaluates each hypothetical object configuration with the set of available detections without to explicitly compute their data association. Tanuja Kayarga "Multiple Object Detection and Tracking in Dynamic Environment using Real Time Video" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-2 | Issue-1 , December 2017, URL: https://www.ijtsrd.com/papers/ijtsrd7181.pdf
16
C, Tejas Rao, Mohammed Zainuddin, Shrishail M. Patil, Shashank G, and Nimrita Koul. "Real Time Person Detection and Classification using YOLO." International Journal of Engineering and Advanced Technology 8, no. 5s (2019): 36–39. http://dx.doi.org/10.35940/ijeat.e1008.0585s19.
Full textAbstract:
A Convolutional Neural Network (CNN) is a class of deep neural network most commonly used in analyzing visual images. Various systems and applications have been built to detect and classify the objects in a faster way taking CNN as its foundation. In this paper, we introduce a model to identify and classify people wearing ID card.Our model uses an object detection system called YOLO (You Only Look Once) for detecting and classifying objects in real-time videos. In the YOLO algorithm, a single convolutional network predicts the bounding boxes and the class probabilities for these boxes. We aim to use our model for authentication, surveillance and security purposes at organizations, corporations and educational institutions to detect an unauthorized person at the premises or somebody without a valid identification document. Using the object detection and classification, we aim to build a model which would alert the respective authorities on the matter.
17
Hilal, Adnan Fadhil, alkhodre Ahmad, and Mohammed Kulal Alyousef Haitham. "Robust and Sensitive Video Motion Detection for security purpose analysis." Journal of Information Sciences and Computing Technologies 1, no. 1 (2015): 69–77. https://doi.org/10.5281/zenodo.4014574.
Full textAbstract:
Today theft and crimes rates are very high, so there is a significant need to monitor and monitor our property. Cameras usage was limited to surveillance; nowadays we don’t, merely, need to monitor our offices, banks and houses; but we also need to lessen and /or prevent damages; including as theft, and other types of crimes. Cameras can track pedestrians, monitor traffic and city management, etc. These cameras send the video frames in real time to our system; the latter detects the moving objects within the frame, to decide whether the moving object is a human or not. The research aims to identify the requirements to develop an Anti-theft Camera real-time recorder for personal property surveillance, in addition to try and evaluate the proposed Anti-theft Camera real-time recorder system. Our goal is to discover and analyze the pedestrian (movement), whatever the lighting or camera angle , by analyzing the video then restart an alarm to prevent the thieves all in real time, even though if there are some factors of disruption .In this research we have developed a system to detect a moving objects within the video frame depend on developing subtracting the current image coming from the video frame from the stored reference image (or the previous image coming from the video frame); it is called "motion blocks detection". To lessen to complexity to become more effective in real time We have got relative highly precise results; (after conducting a trial over 50 moving objects within the image). The results approximate the 50 actually detected moving objects in the video. This has been achieved through improving and analyzing images taken from the video frame. We got a high accuracy results relatively, through applying multiple algorithms to detect motion within video frame.
18
Macherla, Prasanna Kumar, Anitha Telagareddi, Nirmal Kollipara, and Hima Bindu Bommareddy. "Real Time Moving Object Detection Using YOLO." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 1799–802. https://doi.org/10.22214/ijraset.2025.67304.
Full textAbstract:
Abstract: The YOLOv10 explores a cutting-edge advancement in real-time object detection, widely used in robotics, autonomous vehicles, and surveillance for its enhanced speed and accuracy. YOLOv10 builds on earlier versions by integrating improved convolutional layers, anchor boxes, and transformer-based modules, enabling more efficient object identification in a single neural network run, ideal for time- sensitive applications. The research examines advanced training techniques such as refined data augmentation, optimization, and novel loss functions, with tests on datasets like COCO and PASCAL VOC showing superior accuracy in complex environments, including extreme occlusions and dynamic lighting. Key findings highlight YOLOv10's improved detection accuracy, faster processing, and robustness, as well as its scalability for diverse hardware configurations, making it crucial for intelligent systems in dynamic real-world contexts. These have some Limitations ,Those are The number of objects YOLOv10 can find in an image depends on things like how complicated the scene is, the size of the objects, and if they are blocking each other. However, YOLOv10 is very efficient and can usually detect many objects— sometimes dozens or even hundreds—at once, as long as they are in the categories it has been trained to recognize.
19
Zhang, Chen, and Xu Qian. "Real-Time Object Detection Algorithm Based on Back-Projection." Applied Mechanics and Materials 373-375 (August 2013): 483–86. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.483.
Full textAbstract:
Object detection is the important foundation of visual tracking. In this paper, a real-time object detection algorithm based on back-projection was presented. Firstly, according to the principle of back-projection, the objects probability image is calculated by objects color histogram model, and then we determine the object on the basis of some contour strategy in that image. Experimental results show that the proposed algorithm accurately detected the position of object in real-time if the contour of object change within a certain range and the color of object is distinct.
20
L, Dr Priya, Poornimathi K, and Dr P. Kumar. "Enhancing Occlusion Handling in Real-Time Tracking Systems through Geometric Mapping and 3D Reconstruction Validation." International Journal of Engineering and Advanced Technology 12, no. 6 (2023): 7–13. http://dx.doi.org/10.35940/ijeat.f4259.0812623.
Full textAbstract:
Object detection is a classic research problem in the area of Computer Vision. Many smart world applications, like, video surveillance or autonomous navigation systems require a high accuracy in pose detection of objects. One of the main challenges in Object detection is the problem of detecting occluded objects and its respective 3D reconstruction. The focus of this paper is inter-object occlusion where two or more objects being tracked occlude each other. A novel algorithm has been proposed for handling object occlusion by using the technique of geometric matching and its 3D projection obtained. The developed algorithm has been tested using sample data and the results are presented.
21
Dr., Priya.L, Poornimathi.K, and P. Kumar Dr. "Enhancing Occlusion Handling in Real-Time Tracking Systems through Geometric Mapping and 3D Reconstruction Validation." International Journal of Engineering and Advanced Technology (IJEAT) 12, no. 6 (2023): 7–13. https://doi.org/10.35940/ijeat.F4259.0812623.
Full textAbstract:
<strong>Abstract: </strong>Object detection is a classic research problem in the area of Computer Vision. Many smart world applications, like, video surveillance or autonomous navigation systems require a high accuracy in pose detection of objects. One of the main challenges in Object detection is the problem of detecting occluded objects and its respective 3D reconstruction. The focus of this paper is inter-object occlusion where two or more objects being tracked occlude each other. A novel algorithm has been proposed for handling object occlusion by using the technique of geometric matching and its 3D projection obtained. The developed algorithm has been tested using sample data and the results are presented.
22
Sai, T. Varun, B. Aditya, A. Mahendra Reddy, and Dr Y. Srinivasulu. "Real Time Object Detection Using Raspberry Pi." International Journal for Research in Applied Science and Engineering Technology 11, no. 1 (2023): 834–38. http://dx.doi.org/10.22214/ijraset.2023.48549.
Full textAbstract:
Abstract: Due to recent advances in deep learning, the performance of object detection techniques has greatly increased in both speed and accuracy. This enabled highly accurate real-time object detection in modern desktop systems. This project investigates the applicability of working object detection on Raspberry Pi 3. Real-time detection of objects requires a lot of processing power, and achieving real-time speed is a difficult task in a system with limited performance. Many different methods can be used to detect objects. Two methods were implemented in the Raspberry Pi 3 B to determine if they are suitable to work with such weak hardware. An implemented target detector is considered suitable if it achieves a high enough resolution and frame rate to be useful in practical applications. The evaluation performs a number of tests on each detector and measures their performance in terms of detection accuracy, hell time, and frame rate. Raspberry Pi Model B, which is the latest and most powerful product of the Raspberry Pi series, is used as hardware. The camera used is a Raspberry Pi Camera Module v2
23
Vrsalovic, Ivan, Jonatan Lerga, and Marina Ivasic-Kos. "A System for Real-Time Detection of Abandoned Luggage." Sensors 25, no. 9 (2025): 2872. https://doi.org/10.3390/s25092872.
Full textAbstract:
In this paper, we propose a system for the real-time automatic detection of abandoned luggage in an airport recorded by surveillance cameras. To do this, we use an adapted YOLOv11-s model and a proposed algorithm for detecting unattended luggage. The system uses the OpenCV library for the video processing of the recorded footage, a detector, and an algorithm that analyzes the movement of a person and their luggage and evaluates their spatial and temporal relationships to determine whether the luggage is truly abandoned. We used several popular deep convolutional neural network architectures for object detection, e.g., Yolov8, Yolov11, and DETR encoder–decoder transformer with a ResNet-50 deep convolutional backbone, we fine-tuned them on our dataset, and compared their performance in detecting people and luggage in surveillance scenes recorded by an airport surveillance camera. The fine-tuned model significantly improved the detection of people and luggage captured by the airport surveillance camera in our custom dataset. The fine-tuned YOLOv8 and YOLOv11 models achieved excellent real-time results on a challenging dataset consisting only of small and medium-sized objects. They achieved real-time precision (mAP) of over 88%, while their precision for medium-sized objects was over 96%. However, the YOLOv11-s model achieved the highest precision in detecting small objects, corresponding to 85.8%, which is why we selected it as a component of the abandoned luggage detection system. The abandoned luggage detection algorithm was tested in various scenarios where luggage may be left behind and in situations that may be potentially suspicious and showed promising results.
24
Shinde, Prof Shivaji Goroba, and Mr Shubham Suresh Patil. "A Review of Real Time Image Processing for Object Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 07 (2024): 1–14. http://dx.doi.org/10.55041/ijsrem36808.
Full textAbstract:
In past days, capture images with very high quality and good size is so easy because of rapid improvement in quality of capturing device with less costly but superior technology. Videos are a collect of sequential images with a constant time interval. So video can provide also more information about our object when scenarios about to changing with respect to time. Therefore, manually handling videosit can be quite impossible. That time all that need an automatic devise to process these videos. In this thesis one such attempt has been made to track objects in videos. Many algorithms and technology have been developed to automate monitoring the object in a video file. Object detection and tracking is a one of the challenging task in computer vision. Mainly there are three basic steps in video analysis: Detection of objects of Interest from moving objects, Tracking of that interested objects in consecutive frames, and Analysis of object tracks to understand their behavior Some common choice to choose suitable feature to categories, visual objects are intensity, shape, color and feature points. In this thesis, we studied about mean shift tracking based on the color pdf, optical flow tracking based on the intensity and motion; SIFT tracking based on scale invariant local feature points. Keywords: real-time, object detection, tracking, surveillance
25
Mustafa, Fahem Albaghdadi, and Ebady Manaa Mehdi. "Unmanned aerial vehicles and machine learning for detecting objects in real time." Bulletin of Electrical Engineering and Informatics 11, no. 6 (2022): 3490~3497. https://doi.org/10.11591/eei.v11i6.4185.
Full textAbstract:
An unmanned aerial vehicle (UAV) image recognition system in real-time is proposed in this study. To begin, the you only look once (YOLO) detector has been retrained to better recognize objects in UAV photographs. The trained YOLO detector makes a trade-off between speed and precision in object recognition and localization to account for four typical moving entities caught by UAVs (cars, buses, trucks, and people). An additional 1500 UAV photographs captured by the embedded UAV camera are fed into the YOLO, which uses those probabilities to estimate the bounding box for the entire image. When it comes to object detection, the YOLO competes with other deep-learning frameworks such as the faster region convolutional neural network. The proposed system is tested on a wild test set of 1500 UAV photographs with graphics processing unit GPU acceleration, proving that it can distinguish objects in UAV images effectively and consistently in real-time at a detection speed of 60 frames per second.
26
H, Naveen, and Chirra Udayasri. "Real Time Human Object Detection Using Deep Learning." International Journal of Innovative Research in Information Security 11, no. 02 (2025): 117–25. https://doi.org/10.26562/ijiris.2025.v1102.09.
Full textAbstract:
The advancement of technology has enabled machines to perform tasks traditionally reserved for humans. One such area is object and human detection, where systems can identify and track entities in real time. Thanks to technological advancements, machines can now carry out duties that were previously only performed by people. Object and human detection is one such field where systems are able to recognize and follow entities in real time. In order to differentiate between people and different things in video footage, this research makes use of computer vision and deep learning algorithms. By processing each frame quickly and accurately, we guarantee effective detection by utilizing strong techniques like YOLO (You Only Look Once). Applications in surveillance, self-driving cars, and interactive settings are made possible by the suggested system's strong ability to recognize numerous objects and people at once. Numerous contemporary applications, including as surveillance systems, driverless cars, and smart cities, depend on the capacity to automatically recognize and interpret visual features like people and objects. The development of technology has made it possible for machines to carry out jobs that were previously only possible for humans. Object and human detection is one such field where systems are able to recognize and follow entities in real time. In order to differentiate between people and different things in video footage, this research makes use of computer vision and deep learning algorithms. By processing each frame quickly and accurately, we guarantee effective detection by utilizing strong techniques like YOLO (You Only Look Once). Applications in surveillance, self-driving cars, and interactive settings are made possible by the suggested system's strong ability to recognize numerous objects and people at once. Numerous contemporary applications, including as surveillance systems, driverless cars, and smart cities, depend on the capacity to automatically recognize and interpret visual features like people and objects.
27
KANG, SEONGHOON, HYERAN BYUN, and SEONG-WHAN LEE. "REAL-TIME PEDESTRIAN DETECTION USING SUPPORT VECTOR MACHINES." International Journal of Pattern Recognition and Artificial Intelligence 17, no. 03 (2003): 405–16. http://dx.doi.org/10.1142/s0218001403002435.
Full textAbstract:
In this paper, we present a real-time pedestrian detection method in outdoor environments. It is necessary for pedestrian detection to implement obstacle and face detection which are major parts of a walking guidance system for the visually impaired. It detects foreground objects on the ground, discriminates pedestrians from other noninterest objects, and extracts candidate regions for face detection and recognition. For effective real-time pedestrian detection, we have developed a method using stereo-based segmentation and the SVM (Support Vector Machines), which works well particularly in binary classification problem (e.g. object detection). We used vertical edge features extracted from arms, legs and torso. In our experiments, test results on a large number of outdoor scenes demonstrated the effectiveness of the proposed pedestrian detection method.
28
Kyrkou, Christos, and Theocharis Theocharides. "A Flexible Parallel Hardware Architecture for AdaBoost-based Real-Time Object Detection." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 19, no. 6 (2017): 1034–47. https://doi.org/10.1109/TVLSI.2010.2048224.
Full textAbstract:
Real-time object detection is becoming necessary for a wide number of applications related to computer vision and image processing, security, bioinformatics, and several other areas. Existing software implementations of object detection algorithms are constrained in small-sized images and rely on favorable conditions in the image frame to achieve real-time detection frame rates. Efforts to design hardware architectures have yielded encouraging results, yet are mostly directed towards a single application, targeting specific operating environments. Consequently, there is a need for hardware architectures capable of detecting several objects in large image frames, and which can be used under several object detection scenarios. In this work, we present a generic, flexible parallel architecture, which is suitable for all ranges of object detection applications and image sizes. The architecture implements the AdaBoost-based detection algorithm, which is considered one of the most efficient object detection algorithms. Through both field-programmable gate array emulation and large-scale implementation, and register transfer level synthesis and simulation, we illustrate that the architecture can detect objects in large images (up to 1024 × 768 pixels) with frame rates that can vary between 64-139 fps for various applications and input image frame sizes.
29
Berlikozha, Bauyrzhan, Azamat Serek, Beibut Amirgaliyev, Miras Mussabek, and Ainur Zhumadillayeva. "COMPREHENSIVE EVALUATION OF REAL-TIME OBJECT DETECTION ALGORITHM BASED ON EXTENDED CRITERIA." Вестник КазАТК 134, no. 5 (2024): 239–46. https://doi.org/10.52167/1609-1817-2024-134-5-239-246.
Full textAbstract:
In this study, we implemented YOLO (You Only Look Once) for real-time object detection and evaluated its performance based on key metrics such as processing speed, frame rate, and object detection accuracy. Our approach emphasizes both the precision and efficiency of YOLO, focusing on its ability to detect objects in real-world scenarios while maintaining a low computational cost. To identify and count objects, the YOLO algorithm was applied to analyze three images. It divided each image into a grid, and each cell predicted bounding boxes and confidence scores for potential objects. Following the processing of these predictions using non-max suppression to remove duplicates, each image contained an accurate count of the items that were detected. The model achieved a processing time of 17.68 seconds, with an average of 0.25 seconds per frame, demonstrating the system's capability for rapid object detection in near real-time applications. On average, 1.32 objects were detected per frame, with a maximum of 1.67 objects in a single frame and a minimum of 1 object per frame, indicating consistent detection across the dataset. The standard deviation of objects per frame (0.113) shows a low variability in object detection rates, reflecting the robustness of the model in handling diverse input frames. The achieved frame rate of 4.2 FPS demonstrates the model's potential for real-time applications, particularly in environments where processing speed is critical. The scientific novelty of this work lies in demonstrating YOLO’s adaptability for efficient object detection while maintaining high detection rates and consistent performance across varying scenarios. This study contributes to the field by showcasing YOLO's applicability in real-time systems, where object detection speed and accuracy are paramount. Our findings provide a foundation for further optimization in high-performance, low-latency object detection tasks, as well as its scalability for more complex detection systems. The results underscore YOLO's potential in both academic and industrial settings.
30
Krerngkamjornkit, Rapee, and Milan Simic. "Multi Object Detection and Tracking from Video File." Applied Mechanics and Materials 533 (February 2014): 218–25. http://dx.doi.org/10.4028/www.scientific.net/amm.533.218.
Full textAbstract:
This paper describes computer vision algorithms for detection, identification, and tracking of moving objects in a video file. The problem of multiple object tracking can be divided into two parts; detecting moving objects in each frame and associating the detections corresponding to the same object over time. The detection of moving objects uses a background subtraction algorithm based on Gaussian mixture models. The motion of each track is estimated by a Kalman filter. The video tracking algorithm was successfully tested using the BIWI walking pedestrians datasets [. The experimental results show that system can operate in real time and successfully detect, track and identify multiple targets in the presence of partial occlusion.
31
Kim, Jinsoo, and Jeongho Cho. "Exploring a Multimodal Mixture-Of-YOLOs Framework for Advanced Real-Time Object Detection." Applied Sciences 10, no. 2 (2020): 612. http://dx.doi.org/10.3390/app10020612.
Full textAbstract:
To construct a safe and sound autonomous driving system, object detection is essential, and research on fusion of sensors is being actively conducted to increase the detection rate of objects in a dynamic environment in which safety must be secured. Recently, considerable performance improvements in object detection have been achieved with the advent of the convolutional neural network (CNN) structure. In particular, the YOLO (You Only Look Once) architecture, which is suitable for real-time object detection by simultaneously predicting and classifying bounding boxes of objects, is receiving great attention. However, securing the robustness of object detection systems in various environments still remains a challenge. In this paper, we propose a weighted mean-based adaptive object detection strategy that enhances detection performance through convergence of individual object detection results based on an RGB camera and a LiDAR (Light Detection and Ranging) for autonomous driving. The proposed system utilizes the YOLO framework to perform object detection independently based on image data and point cloud data (PCD). Each detection result is united to reduce the number of objects not detected at the decision level by the weighted mean scheme. To evaluate the performance of the proposed object detection system, tests on vehicles and pedestrians were carried out using the KITTI Benchmark Suite. Test results demonstrated that the proposed strategy can achieve detection performance with a higher mean average precision (mAP) for targeted objects than an RGB camera and is also robust against external environmental changes.
32
Tamrakar, Vishesh. "Real-Time Object Detection and Recognition with Computer Vision." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 1677–81. http://dx.doi.org/10.22214/ijraset.2024.61921.
Full textAbstract:
Abstract: Object detection, a fundamental aspect of computer vision, is essential for identifying and localizing objects within images or video frames, leveraging advancements in deep learning, particularly convolutional neural networks (CNNs), to enhance precision and speed. Its applications span diverse domains, from autonomous vehicles and surveillance systems to augmented reality and human-computer interaction. Our project focuses on engineering a real-time object detection system, integrating deep learning and computer vision methodologies. Anchored on the robust Single Shot Multibox Detector (SSD) architecture and reinforced by the efficiency and accuracy of the MobileNetV3 backbone, our system utilizes a pre-trained SSD MobileNetV3 model and comprehensive annotations from the COCO dataset to adeptly detect and recognize a wide array of objects within live video streams or archived footage. It seamlessly processes video frames from various sources, annotating detected objects in real-time to provide instant visual feedback. Offering customizable confidence thresholds and support for multiple video sources, our project showcases the transformative potential of deep learning and computer vision, advancing realtime object detection across domains like surveillance and interactive systems. By pushing the boundaries of object detection technology, our project aims to enhance safety, efficiency, and user experiences in various applications, promising to redefine the landscape of computer vision with innovation and advancement.
33
Oh, Sang-Young, and Seon-Min Hwang. "Real-time collision detection for dynamic objects." Journal of the Korea Academia-Industrial cooperation Society 9, no. 3 (2008): 717–20. http://dx.doi.org/10.5762/kais.2008.9.3.717.
Full text
34
Abiamamela, Obi-Obuoha, Samuel Rizama Victor, Okafor Ifeanyichukwu, Edore Ovwenkekpere Haggai, Obe Kehinde, and Ekundayo Jeremiah. "Real-time traffic object detection using detectron 2 with faster R-CNN." World Journal of Advanced Research and Reviews 24, no. 2 (2024): 2173–89. https://doi.org/10.5281/zenodo.15118295.
Full textAbstract:
Object detection is becoming more and more important in daily life, especially in applications like advanced traffic analysis, intelligent driver assistance systems, and driverless cars. The accurate identification of objects from real-time video is crucial for effective traffic analysis. These systems play a vital role in providing drivers and authorities a comprehensive understanding of the road and surrounding environment. Modern algorithms and neural network-based architecture with extremely high detection accuracy, like Faster R-CNN are crucial to achieving this. This study investigates an advanced object detection system designed for urban traffic applications using an interactive Gradio interface and Detectron2’s Faster R-CNN model. The research focuses on developing a model capable of identifying key traffic objects such as traffic lights, vehicles, buses, crossroads etc., with high accuracy and precision. A significant contribution of this study is the integration of Gradio-based interface that enables users to upload images or videos from their local storage or webcam and view the results in real time making the model both accessible and practical. Our findings demonstrate that the Detectron2 framework, paired with Gradio’s interactive interface offers a reliable and scalable solution for traffic monitoring and safety applications.
35
Rajeshwar, Kumar Dewangan, and Chaubey Siddharth. "Real-time object detector for the visually impaired with voice feedback using openCV." i-manager's Journal on Digital Signal Processing 10, no. 1 (2022): 29. http://dx.doi.org/10.26634/jdp.10.1.18580.
Full textAbstract:
The goal of this paper is to create an object detector model that can detect objects for visually impaired people and other commercial users by detecting it at a certain distance. Existing object detection algorithms required a huge amount of training data, which took longer and was extremely complex. This is also a difficult task. As a result, it presents a computer vision paradigm for converting an object to text by importing a pre-trained CAFFEMODEL (a machine learning model created by Caffe) framework dataset model, and the texts are further converted to speech. This method allows the detection of multiple objects on the same screen. It helps in real-time object detection. This paper discusses the concept, methodology, and system architecture for the implementation of the system in combination with the obtained intermediate results and analyzes the tools used in the proposed system. This system can then be implemented in any other system. Portable gadgets that detect objects at a certain distance from visually impaired people and transmit a voice signal.
36
Kumar, Aayush, Amit Kumar, Avanish Chandra, and Indira Adak. "Custom Object Detection and Analysis in Real Time: YOLOv4." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3982–90. http://dx.doi.org/10.22214/ijraset.2022.43303.
Full textAbstract:
Abstract: Object recognition is one of the most basic and complex problems in computer vision, which seeks to locate object instances from the enormous categories of already defined and readily available natural images. The object detection method aims to recognize all the objects or entities in the given picture and determine the categories and position information to achieve machine vision understanding. Several tactics have been put forward to solve this problem, which is more or less inspired by the principles based on Open Source Computer Vision Library (OpenCV) and Deep Learning. Some are relatively good, while others fail to detect objects with random geometric transformations. This paper proposes demonstrating the " HAWKEYE " application, a small initiative to build an application working on the principle of EEE i.e. (Explore→Experience→Evolve). Keywords: Convolution Neural Network, Object detection, Image classification, Deep learning, Open CV, Yolov4.
37
Juyal, Amit. "A Deep Learning-Based Approach for Real-Time Object Detection and Recognition." Mathematical Statistician and Engineering Applications 70, no. 2 (2021): 1304–14. http://dx.doi.org/10.17762/msea.v70i2.2322.
Full textAbstract:
Object detection and recognition is an essential task in computer vision with numerous real-world applications such as surveillance, self-driving cars, and robotics. In recent years, deep learning-based approaches have significantly improved the accuracy and speed of object detection and recognition. The You Only Look Once version 3 (YOLOv3) algorithm is a popular deep learning-based approach that can detect and recognize objects in real-time. The Common Objects in Context (COCO) dataset is a large-scale dataset with over 330,000 labeled images and more than 2.5 million object instances, making it a popular choice for object detection and recognition tasks. In this paper, we propose a deep learning-based approach for real-time object detection and recognition using the YOLOv3 architecture and COCO dataset. We evaluate our approach based on several performance metrics, including mean average precision (mAP), frames per second (FPS), total object detection time, object detection accuracy, false positive rate, number of detected objects, and mean intersection over union (mIoU). Our results show that our approach achieves a mean average precision of 0.76 on the COCO dataset and a real-time performance of 40 frames per second on a single GPU. Additionally, our approach achieves an object detection accuracy of 93.5%, a false positive rate of 6.5%, and a mean intersection over union of 0.65. Our proposed approach shows promising results for real-time object detection and recognition and can be applied to various real-world applications.
38
Zhang, Lili, Zhiqiang Xie, Mengqi Xu, Yi Zhang, and Gaoxu Wang. "EYOLOv3: An Efficient Real-Time Detection Model for Floating Object on River." Applied Sciences 13, no. 4 (2023): 2303. http://dx.doi.org/10.3390/app13042303.
Full textAbstract:
At present, the surveillance of river floating in China is labor-intensive, time-consuming, and may miss something, so a fast and accurate automatic detection method is necessary. The two-stage convolutional neural network models appear to have high detection accuracy, but it is hard to reach real-time detection, while on the other hand, the one-stage models are less time-consuming but have lower accuracy. In response to the above problems, we propose a one-stage object detection model EYOLOv3 to achieve real-time and high accuracy detection of floating objects in video streams. Firstly, we design a multi-scale feature extraction and fusion module to improve the feature extraction capability of the network. Secondly, a better clustering algorithm is used to analyze the size characteristics of floating objects to design the anchor box, enabling the network to detect objects more effectively. Then a focus loss function is proposed to make the network effectively overcome the sample imbalance problem, and finally, an improved NMS algorithm is proposed to solve the object suppressed problem. Experiments show that the proposed model is efficient in detection of river floating objects, and has better performance than the classical object detection method and the latest method, realizing real-time floating detection in video streams.
39
Ennaama, Sara, Hassan Silkan, Ahmed Bentajer, and Abderrahim Tahiri. "Enhanced Real-Time Object Detection using YOLOv7 and MobileNetv3." Engineering, Technology & Applied Science Research 15, no. 1 (2025): 19181–87. https://doi.org/10.48084/etasr.8777.
Full textAbstract:
Object detection serves as a crucial element in computer vision, increasingly relying on deep learning techniques. Among various methods, the YOLO series has gained recognition as an effective solution. This research enhances object detection by merging YOLOv7 with MobileNetv3, known for its efficiency and feature extraction. The integrated model was tested using the COCO dataset, which contains over 164,000 images across 80 categories, achieving a mAP score of 0.61. Additionally, confusion matrix analysis confirmed its accuracy, especially in detecting common objects such as 'person' and 'car' with minimal misclassifications. The results demonstrate the potential of the proposed model to address the complexities of real-world scenarios, highlighting its applicability in various scientific and industrial domains.
40
Lakshmi, Mrs Ch Naga, Puvvula Vamsi Pravallika, Ponnuri Sandhya, Paritala Venkata Lavanya, and Padamata Prasanth. "YOLOv11: A Next-Generation Approach to Real-Time Object Detection." Journal of Nonlinear Analysis and Optimization 16, no. 01 (2025): 748–54. https://doi.org/10.36893/jnao.2025.v16i01.089.
Full textAbstract:
Vision impairment is one of the top ten disabilities that can be affected by humans, with India having the highest visually impaired population. This study introduces a novel framework utilizing YOLOv11 for real-time object detection and recognition for the objects and to assist the visually impaired individuals in navigating their surroundings independently. The YOLOv11 detector, trained on the COCO dataset with an additional class for enhanced detection, enables precise classification of various objects. The system integrates Python and OpenCV to detect objects from videos and webcams, ultimately providing audio feedback to the user. The proposed system ensures fast and accurate detection, improving accessibility and reducing dependency on external assistance.
41
Gunawan, Chichi Rizka, Nurdin Nurdin, and Fajriana Fajriana. "Design of A Real-Time Object Detection Prototype System with YOLOv3 (You Only Look Once)." International Journal of Engineering, Science and Information Technology 2, no. 3 (2022): 96–99. http://dx.doi.org/10.52088/ijesty.v2i3.309.
Full textAbstract:
Object detection is an activity that aims to gain an understanding of the classification, concept estimation, and location of objects in an image. As one of the fundamental computer vision problems, object detection can provide valuable information for the semantic understanding of images and videos and is associated with many applications, including image classification. Object detection has recently become one of the most exciting fields in computer vision. Detection of objects on this system using YOLOv3. The You Only Look Once (YOLO) method is one of the fastest and most accurate methods for object detection and is even capable of exceeding two times the capabilities of other algorithms. You Only Look Once, an object detection method, is very fast because a single neural network predicts bounded box and class probabilities directly from the whole image in an evaluation. In this study, the object under study is an object that is around the researcher (a random thing). System design using Unified Modeling Language (UML) diagrams, including use case diagrams, activity diagrams, and class diagrams. This system will be built using the python language. Python is a high-level programming language that can execute some multi-use instructions directly (interpretively) with the Object Oriented Programming method and also uses dynamic semantics to provide a level of syntax readability. As a high-level programming language, python can be learned easily because it has been equipped with automatic memory management, where the user must run through the Anaconda prompt and then continue using Jupyter Notebook. The purpose of this study was to determine the accuracy and performance of detecting random objects on YOLOv3. The result of object detection will display the name and bounding box with the percentage of accuracy. In this study, the system is also able to recognize objects when they object is stationary or moving.
42
Korubilli, Laxmi Swaroopa, Ramya Sri Lingoji, and Agrawal Madhuri. "Real-Time Traffic Observation and Analysis System." International Journal of Scientific Development and Research 9, no. 6 (2024): 183–89. https://doi.org/10.5281/zenodo.11547315.
Full textAbstract:
This paper introduces a real-time traffic observation and analysis system empowered by the You Only Look Once (YOLO) object detection model and the Bytetrack algorithm for vehicle tracking. The system is designed to monitor traffic parameters such as vehicle speed, traffic flow rate, and congestion levels using video footage of roadways. YOLO is utilized for real-time object detection. Bytetrack employs the Kalman filter to predict future vehicle positions and association techniques for mapping vehicles across successive frames, facilitating the assignment of tracking IDs. Leveraging the YOLO model from the Ultralytics organization, the system provides accurate and efficient traffic monitoring and analysis, enhancing decision-making for urban traffic management. When provided with a video of a road to the system the system gives as output a video which includes the inputted footage with added information. It marks each identified vehicle with a square. It also displays the count of each vehicle on top of the square along with the calculated speed of each vehicle. The system will use the computer vision model YOLO ( You Only Look Once ) for vehicle detection. The famous object detection model YOLO is a single convolutional neural network whose implementation comes pre-trained with over 80 objects. The YOLO model can identify all the objects involved in the input image by passing the image through a single convolutional neural network only once. The model does not identify the potential object locations using a neural network and then uses another neural network to detect if an object is present in all the predicted areas. So YOLO is very quick compared to other computer vision models. The effective architecture of the model allows us to perform real-time detections. So we are using the YOLO model for vehicle detection in our project. The system then uses the object tracking algorithm ByteTrack to track the detected vehicles. The output contains the total number of vehicles identified in the footage, the count of the number of vehicles entering and leaving the road, the traffic flow rate, and the average speed of the traffic. Therefore the system presents us with a set of observations which would be beneficial in getting a basic understanding of the traffic conditions of the road.
43
Llano, Christian R., Yuan Ren, and Nazrul I. Shaikh. "Object Detection and Tracking in Real Time Videos." International Journal of Information Systems in the Service Sector 11, no. 2 (2019): 1–17. http://dx.doi.org/10.4018/ijisss.2019040101.
Full textAbstract:
Object and human tracking in streaming videos are one of the most challenging problems in vision computing. In this article, we review some relevant machine learning algorithms and techniques for human identification and tracking in videos. We provide details on metrics and methods used in the computer vision literature for monitoring and propose a state-space representation of the object tracking problem. A proof of concept implementation of the state-space based object tracking using particle filters is presented as well. The proposed approach enables tracking objects/humans in a video, including foreground/background separation for object movement detection.
44
Qiu, Zhi, Junyuan Zeng, Wenhui Tang, Houcheng Yang, Junjun Lu, and Zuoxi Zhao. "Research on Real-Time Automatic Picking of Ground-Penetrating Radar Image Features by Using Machine Learning." Horticulturae 8, no. 12 (2022): 1116. http://dx.doi.org/10.3390/horticulturae8121116.
Full textAbstract:
Hard foreign objects such as bricks, wood, metal materials, and plastics in orchard soil can affect the operational safety of garden machinery. Ground-Penetrating Radar (GPR) is widely used for the detection of hard foreign objects in soil due to its advantages of non-destructive detection (NDT), easy portability, and high efficiency. At present, the degree of automatic identification applied in soil-oriented foreign object detection based on GPR falls short of the industry’s expectations. To further enhance the accuracy and efficiency of soil-oriented foreign object detection, we combined GPR and intelligent technology to conduct research on three aspects: acquiring real-time GPR images, using the YOLOv5 algorithm for real-time target detection and the coordinate positioning of GPR images, and the construction of a detection system based on ground-penetrating radar and the YOLOv5 algorithm that automatically detects target characteristic curves in ground-penetrating radar images. In addition, taking five groups of test results of detecting different diameters of rebar inside the soil as an example, the obtained average error of detecting the depth of rebar using the detection system is within 0.02 m, and the error of detecting rebar along the measuring line direction from the location of the starting point of GPR detection is within 0.08 m. The experimental results show that the detection system is important for identifying and positioning foreign objects inside the soil.
45
Tripathi, Akash, T. V. Ajay Kumar, Tarun Kanth Dhansetty, and J. Selva Kumar. "Real Time Object Detection using CNN." International Journal of Engineering & Technology 7, no. 2.24 (2018): 33. http://dx.doi.org/10.14419/ijet.v7i2.24.11994.
Full textAbstract:
Achieving new heights in object detection and image classification was made possible because of Convolution Neural Network(CNN). However, compared to image classification the object detection tasks are more difficult to analyze, more energy consuming and computation intensive. To overcome these challenges, a novel approach is developed for real time object detection applications to improve the accuracy and energy efficiency of the detection process. This is achieved by integrating the Convolutional Neural Networks (CNN) with the Scale Invariant Feature Transform (SIFT) algorithm. Here, we obtain high accuracy output with small sample data to train the model by integrating the CNN and SIFT features. The proposed detection model is a cluster of multiple deep convolutional neural networks and hybrid CNN-SIFT algorithm. The reason to use the SIFT featureis to amplify the model‟s capacity to detect small data or features as the SIFT requires small datasets to detect objects. Our simulation results show better performance in accuracy when compared with the conventional CNN method. As the resources like RAM, graphic card, ROM, etc. are limited we propose a pipelined implementation on an aggregate Central Processing Unit(CPU) and Graphical Processing Unit(GPU) platform.
46
Bais, Kunalsingh. "Camouflaged Object Detection System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40691.
Full textAbstract:
The detection of camouflaged objects is crucial for applications in surveillance, wildlife monitoring, and military scenarios, where objects blend seamlessly into their surroundings. This review consolidates 15 influential research studies covering advancements in datasets, models, real-time detection technologies, and multimodal approaches. The focus is on implementing YOLOv8, a state-of-the-art real-time object detection model, using the ACD1K dataset, which is specifically designed for military surveillance. By synthesizing methodologies, evaluation metrics, and applications, this paper highlights significant progress in camouflaged object detection (COD) and identifies ongoing challenges in computational efficiency, dataset diversity, and real-world adaptability. Key Words: Camouflaged object detection, YOLOv8, ACD1K dataset, military surveillance, real-time detection.
47
Padala, Srinivas, K. Lakshmi Durga, G. Divya Hari Durga, Seereddi V. Satya Sai Chandra Sri Madhu, and Eedara Rohith. "REAL-TIME OBJECT DETECTION MODEL USING YOLOv10." Fuzzy Systems and Soft Computing 20, no. 01 (2025): 299–309. https://doi.org/10.36893/fssc.2025.v20i01.032.
Full textAbstract:
From security systems to driverless cars, object detection is essential to many applications. The main goal of this project is to use YOLOv10 and RCNN (Region-Convolutional Neural Network) to perform YOLO (You Only Look Once) object identification in a Flask web application. With notable speed and accuracy gains over its predecessors, YOLOv10 is a state-of-the-art iteration of the YOLO model intended for quick and precise real-time object recognition. Furthermore, by combining region suggestions with CNN for feature extraction, the study integrates RCNN for more accurate object localization. Users can contribute photos or video streams for object detection using these models, which are incorporated into a web application built with Flask. After processing these inputs and performing detection, the application shows the findings along with bounding boxes and recognized objects. Making use of RCNN's and YOLOv10's advantages, the suggested system makes sure that real-time performance and detection accuracy are balanced. The result is a reliable, effective, and user-friendly solution for object detection in practical situations.
48
Khan, Mr M. S., Ritul Ghumare, Prerna Malode, Rutuja Murkute, and Sakshi Pagare. "Research on Real -Time Object Detection with Speech Feedback for Visually Impaired." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 2117–24. https://doi.org/10.22214/ijraset.2025.68713.
Full textAbstract:
Abstract: It is widely recognized that approximately 285 million people worldwide are visually impaired, which accounts for nearly 20% of India's population. Many of these individuals rely heavily on others to fulfill even their most basic daily needs. In our project, we utilized TensorFlow, a cutting-edge library developed by Google, to power our neural network models. The TensorFlow Object Detection API was employed to detect various objects in real-time. Additionally, we introduced the YOLO algorithm, which plays a pivotal role in object detection. During training, YOLO matches anchor boxes with the bounding boxes of ground truth objects in an image. The anchor box with the highest overlap with an object is responsible for predicting that object's class and location. Our system also integrates a microcontroller equipped with a Wi-Fi module for seamless communication. This user-friendly interface allows visually impaired users to navigate both indoor and outdoor environments with ease. To further enhance the user experience and reduce navigation challenges, we incorporated an obstacle detection system using ultrasonic sensors. This system identifies nearby obstacles and alerts the user, helping them avoid potential hazards and navigate with greater confidence.
49
Shin, Yeonha, Heesub Shin, Jaewoo Ok, Minyoung Back, Jaehyuk Youn, and Sungho Kim. "DCEF2-YOLO: Aerial Detection YOLO with Deformable Convolution–Efficient Feature Fusion for Small Target Detection." Remote Sensing 16, no. 6 (2024): 1071. http://dx.doi.org/10.3390/rs16061071.
Full textAbstract:
Deep learning technology for real-time small object detection in aerial images can be used in various industrial environments such as real-time traffic surveillance and military reconnaissance. However, detecting small objects with few pixels and low resolution remains a challenging problem that requires performance improvement. To improve the performance of small object detection, we propose DCEF 2-YOLO. Our proposed method enables efficient real-time small object detection by using a deformable convolution (DFConv) module and an efficient feature fusion structure to maximize the use of the internal feature information of objects. DFConv preserves small object information by preventing the mixing of object information with the background. The optimized feature fusion structure produces high-quality feature maps for efficient real-time small object detection while maximizing the use of limited information. Additionally, modifying the input data processing stage and reducing the detection layer to suit small object detection also contributes to performance improvement. When compared to the performance of the latest YOLO-based models (such as DCN-YOLO and YOLOv7), DCEF 2-YOLO outperforms them, with a mAP of +6.1% on the DOTA-v1.0 test set, +0.3% on the NWPU VHR-10 test set, and +1.5% on the VEDAI512 test set. Furthermore, it has a fast processing speed of 120.48 FPS with an RTX3090 for 512 × 512 images, making it suitable for real-time small object detection tasks.
50
Krishna, Ram. "Real Time Human Body Posture Analysis Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 2951–55. http://dx.doi.org/10.22214/ijraset.2023.52099.
Full textAbstract:
Abstract: We present a novel approach for accurately estimating the pose of objects in a low-cost and resource-efficient manner, making it suitable for deployment on embedded systems. Our algorithm comprises of two primary stages: object detection and spatial reconstruction. In the first stage, we employ a Convolutional Neural Network (CNN) called PoseNet for object detection. This approach has proven to be effective in detecting and localizing objects in an image. Next, utilizing stereo correspondences, we 3D reconstruct the spatial coordinates of multiple ORB features within the object's bounding box. This enables us to accurately estimate the position of the object in space. To calculate the final position of the object, we compute a weighted average of the stereo-corresponded key points' spatial coordinates. The weights are proportional to the level of ORB stereo matching, which enables us to obtain a more accurate estimate of the object's position in space. Our algorithm was tested in a calibrated environment, and we compared the results with a deep learning-based method using various datasets. The results show that our approach outperforms existing methods in terms of accuracy, while maintaining a low cost and efficient resource utilization. Our proposed method has several applications, including the quantitative and qualitative analysis of human posture. By analyzing all aspects of a person's posture, we can determine if there are any postural deviations, imbalances, or muscle weaknesses that may be causing pain or discomfort. This information can then be used to develop personalized rehabilitation programs, reducing the risk of injury and enhancing athletic performance. Furthermore, our approach can be used in various assistive technology applications, such as the control of robotic arms for pick-andplace tasks. The low-cost and resource-efficient nature of our algorithm make it ideal for deployment in embedded systems, enabling us to develop affordable and accessible assistive technology solutions. In conclusion, our proposed algorithm provides an accurate, low-cost, and resource-efficient solution for pose estimation, with a wide range of potential applications in human posture analysis, assistive technology, and beyond.