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Relevant bibliographies by topics / Object detection.Convolutional neural networks. YOLO. Deep learning.Computer vision / Journal articles

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Journal articles on the topic 'Object detection.Convolutional neural networks. YOLO. Deep learning.Computer vision'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

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1

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.

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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, automat

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M., Chinnarao R. Goutham Sai Kalyan T. Naga Pravallika B. Srinivas. "Object Detection Using Yolo And Tensor Flow." International Journal in Engineering Sciences 1, no. 1 (2024): 13–23. https://doi.org/10.5281/zenodo.11825059.

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Object detection methods aim to identify all target objects in the target image and determine the categories and position information in order to achieve machine vision understanding. Numerous approaches have been proposed to solve this problem, mainly inspired by methods of computer vision and deep learning. However, existing approaches always perform poorly for the detection of small, dense objects, and even fail to detect objects with random geometric transformations. In this study, we compare and analyse mainstream object detection algorithms and propose a multi-scaled deformable convoluti

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Tiwari, Shashank. "Advanced Two Stage AI Technique for Object Detection." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47821.

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Object detection in computer vision uses AI, mainly deep learning, to identify and locate objects in images or videos. It involves an AI system that spots various objects, determines their type, and marks their positions with bounding boxes. Built on advanced deep learning models like Convolutional Neural Networks (CNNs), YOLO, or Faster R-CNN, it excels in real-time detection. Trained on large datasets like COCO, it recognizes diverse objects across different scenes. It tackles challenges investigates challenges like varying object sizes, scales, lighting, and occlusion using techniques such

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R, Prithvi Raj, Rohith M, Ravichandra A R, and Shafien Ulla Khan. "IMAGE CHARACTER RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS." International Research Journal of Computer Science 9, no. 8 (2022): 304–11. http://dx.doi.org/10.26562/irjcs.2022.v0908.29.

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Object Recognition is a challenging and exciting task of computer Vision. Object recognition is to describe a collection of related computer vision tasks which involves identifying objects in images. Image classification involves predicting the class of objects in an image. Object localization refers to identifying the location of objects in an image. Image Annotation in Machine Learning is the process of creating bounding boxes around the localized images. Now with the advance of deep learning and neural networks, we can finally tackle these problems without coming up with various heuristics

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Park, Hee-Mun, and Jin-Hyun Park. "YOLO Network with a Circular Bounding Box to Classify the Flowering Degree of Chrysanthemum." AgriEngineering 5, no. 3 (2023): 1530–43. http://dx.doi.org/10.3390/agriengineering5030094.

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Detecting objects in digital images is challenging in computer vision, traditionally requiring manual threshold selection. However, object detection has improved significantly with convolutional neural networks (CNNs), and other advanced algorithms, like region-based convolutional neural networks (R-CNNs) and you only look once (YOLO). Deep learning methods have various applications in agriculture, including detecting pests, diseases, and fruit quality. We propose a lightweight YOLOv4-Tiny-based object detection system with a circular bounding box to accurately determine chrysanthemum flower h

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Muhammad Zafar Ul Haq, Mukkaram Baig, Ayaan Zaman Khattak, Faizan Asghar, Muhammad Zunnurain Hussain, and Muhammad Zulkifl Hasan. "Redefining Object Detection: Harnessing the Full Potential of YOLO." Annual Methodological Archive Research Review 3, no. 1 (2025): 68–80. https://doi.org/10.63075/r165ne08.

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Recently, there has been a notable use of deep learning methodologies, namely convolutional neural networks (CNNs), in computer vision, specifically about the significant matter of object recognition. The "You Only Look Once" (YOLO) technique is a strategy that offers a rapid and dependable approach for detecting objects in both static and dynamic visual content. This article presents a comprehensive overview of YOLO, including its historical context, architectural design, and performance evaluation on many widely accepted benchmarks within the industry. In addition, we identify the study's li

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Narendra, Joglekar. "Human Detector & Counting." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48230.

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Abstract - Human detection and counting are crucial tasks in computer vision, with applications in security surveillance, crowd monitoring, retail analytics, and smart city planning. This paper explores various approaches to human detection and counting, including traditional image processing techniques and modern deep learning-based methods such as Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO). Challenges such as occlusion, varying lighting conditions, and real-time processing constraints are addressed. The study also highlights the integration of human detection models w

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Ali, Mahmoud Atta Mohammed. "Advancing Crowd Object Detection: A Review of YOLO, CNN, and Vision Transformers Hybrid Approach." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 1240–68. http://dx.doi.org/10.22214/ijraset.2024.63293.

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Abstract: One of the most basic and difficult areas of computer vision and image understanding applications is still object detection. Deep neural network models and enhanced object representation have led to significant progress in object detection. This research investigates in greater detail how object detection has changed in the recent years in the deep learning age. We provide an overview of the literature on a range of cutting-edge object identification algorithms and the theoretical underpinnings of these techniques. Deep learning technologies are contributing to substantial innovation

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Gururaj, Vaishnavi, Shriya Varada Ramesh, Sanjana Satheesh, Ashwini Kodipalli, and Kusuma Thimmaraju. "Analysis of deep learning frameworks for object detection in motion." International Journal of Knowledge-based and Intelligent Engineering Systems 26, no. 1 (2022): 7–16. http://dx.doi.org/10.3233/kes-220002.

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Object detection and recognition is a computer vision technology and is considered as one of the challenging tasks in the field of computer vision. Many approaches for detection have been proposed in the past. AIM: This paper is mainly aiming to discuss the existing detection and classification techniques of Deep Convolutional Neural Networks (CNN) with an importance placed on highlighting the training and accuracy of the different CNN models. METHODS: In the proposed work, Faster RCNN, YOLO and SSD are used to detect helmets. OUTCOME: The survey says MobileNets has higher accuracy when compar

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Kalshetti, Mallinath. "Object Detection and Recognition Using Image Processing." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30262.

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Object detection and recognition are critical problems in computer vision, with numerous applications in areas such as surveillance, autonomous systems, and medical imaging. This study provides a comprehensive overview of object detection and recognition utilizing image processing methods. Object detection is the process of finding and locating objects inside picture or video frames. Traditional approaches were based on handcrafted features and classifiers, but recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have changed the discipline. Architectures su

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Khalid, Saim, Hadi Mohsen Oqaibi, Muhammad Aqib, and Yaser Hafeez. "Small Pests Detection in Field Crops Using Deep Learning Object Detection." Sustainability 15, no. 8 (2023): 6815. http://dx.doi.org/10.3390/su15086815.

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Deep learning algorithms, such as convolutional neural networks (CNNs), have been widely studied and applied in various fields including agriculture. Agriculture is the most important source of food and income in human life. In most countries, the backbone of the economy is based on agriculture. Pests are one of the major challenges in crop production worldwide. To reduce the overall production and economic loss from pests, advancement in computer vision and artificial intelligence may lead to early and small pest detection with greater accuracy and speed. In this paper, an approach for early

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Rao, Shika, and Nitya Mitnala. "Exploring automated object detection methods for manholes using classical computer vision and deep learning." Machine Graphics and Vision 32, no. 1 (2023): 25–53. http://dx.doi.org/10.22630/mgv.2023.32.1.2.

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Open, broken, and improperly closed manholes can pose problems for autonomous vehicles and thus need to be included in obstacle avoidance and lane-changing algorithms. In this work, we propose and compare multiple approaches for manhole localization and classification like classical computer vision, convolutional neural networks like YOLOv3 and YOLOv3-Tiny, and vision transformers like YOLOS and ViT. These are analyzed for speed, computational complexity, and accuracy in order to determine the model that can be used with autonomous vehicles. In addition, we propose a size detection pipeline us

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Naif Alsharabi. "Real-Time Object Detection Overview: Advancements, Challenges, and Applications." مجلة جامعة عمران 3, no. 6 (2023): 12. http://dx.doi.org/10.59145/jaust.v3i6.73.

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Real-time object detection is a crucial aspect of computer vision with applications spanning autonomous vehicles, surveillance, robotics, and augmented reality. This study examines real-time object detection techniques, highlighting their significance in artificial intelligence. The primary goal is swift and accurate object identification in images or video streams. Traditional methods like sliding windows and region-based approaches had limitations in computational efficiency. Deep learning, particularly Convolutional Neural Networks (CNNs), revolutionized object detection. Models like SSD, Y

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Yang, Ke, Baoliang Peng, Fengwei Gu, et al. "Convolutional Neural Network for Object Detection in Garlic Root Cutting Equipment." Foods 11, no. 15 (2022): 2197. http://dx.doi.org/10.3390/foods11152197.

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Traditional manual garlic root cutting is inefficient and can cause food safety problems. To develop food processing equipment, a novel and accurate object detection method for garlic using deep learning—a convolutional neural network—is proposed in this study. The you-only-look-once (YOLO) algorithm, which is based on lightweight and transfer learning, is the most advanced computer vision method for single large object detection. To detect the bulb, the YOLOv2 model was modified using an inverted residual module and residual structure. The modified model was trained based on images of bulbs w

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Rohaan,, Khan. "Helmet Detection System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41490.

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This paper presents a real-time helmet detection system designed to enhance safety in workplaces and traffic environments. Utilizing deep learning models like YOLO (You Only Look Once), the system detects whether individuals are wearing helmets in live video feeds or images. Trained on a diverse dataset, the system achieves high accuracy and efficiency, making it suitable for deployment in safetycritical areas. The paper highlights its potential applications, challenges, and future improvements, such as IoT integration and edge computing, to further enhance performance and scalability. This wo

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Shankar, Reddy Shiva, Rao Venkata Rama Maheswara, Priyadarshini Voosala, and Silpa Nrusimhadri. "You only look once model-based object identification in computer vision." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 827–38. https://doi.org/10.11591/ijai.v13.i1.pp827-838.

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You only look once version 4 (YOLOv4) is a deep-learning object detection algorithm. It is used to decrease parameters and simplify network structures, making it suited for mobile and embedded device development. The YOLO detector can foresee an object's Class, bounding box, and probability of that Object's Class being found inside that bounding box. A probability value for each bounding box represents the likelihood of a given item class in that bounding box. Global features, channel attention, and special attention are also applied to extract more compelling information. Finally, the model c

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Tahir, Arsalan, Hafiz Suliman Munawar, Junaid Akram, et al. "Automatic Target Detection from Satellite Imagery Using Machine Learning." Sensors 22, no. 3 (2022): 1147. http://dx.doi.org/10.3390/s22031147.

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Object detection is a vital step in satellite imagery-based computer vision applications such as precision agriculture, urban planning and defense applications. In satellite imagery, object detection is a very complicated task due to various reasons including low pixel resolution of objects and detection of small objects in the large scale (a single satellite image taken by Digital Globe comprises over 240 million pixels) satellite images. Object detection in satellite images has many challenges such as class variations, multiple objects pose, high variance in object size, illumination and a d

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Sudheer Benarji P. "Early Accident Detection Using Deep Learning Models." Journal of Information Systems Engineering and Management 10, no. 17s (2025): 395–402. https://doi.org/10.52783/jisem.v10i17s.2745.

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Vehicle accidents rank as the most prominent causes of injury and fatality across the globe. Early detection and response can minimize the losses that may otherwise ensue and enhance safety along roads. The last few years have seen exciting progress in computer vision and machine learning, opening new avenues to attack this major problem. It is in this context that this abstract discusses video-based vehicle accident detection with key techniques, challenges, and future directions. The chief objective of video-based vehicle accidents detection is the automatic identification and classification

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Muthuraj, Dr A. "Deep Learning-Based Monitoring of Illegal Plastic Waste Disposal in Urban Areas." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48127.

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Abstract Illegal plastic waste disposal poses a significant environmental and public health challenge in rapidly urbanizing regions. Traditional waste management systems often lack real-time monitoring capabilities, making enforcement and mitigation difficult. This study proposes a deep learning-based surveillance framework that utilizes closed-circuit television (CCTV) footage to automatically detect and classify incidents of plastic waste dumping in urban environments. Leveraging convolutional neural networks (CNNs) and object detection models such as YOLOv8, the system is trained on a custo

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Eliganti, Ramalakshmi, Kasturi Dixitha, and V. Gouthami. "Object Detector for Visually Impaired with Distance Calculation for Humans." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 834–38. https://doi.org/10.35940/ijeat.D7868.049420.

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Object detection is a computer vision technique for locating instances of objects in videos. When we as humans look at images or videos, we can recognize and locate objects within a matter of moments. The main goal of this project is to clone the intelligence of humans in doing that using Deep Neural Networks and IOT, Raspberry Pi and a camera. This model could be used for visually disabled people for improved navigation and crash free motion. When we consider real time scenarios, numerous objects come into a single frame. To identify different items simultaneously as they are captured, a stro

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Xue, Qing. "Advancements in object detection: From machine learning to deep learning paradigms." Applied and Computational Engineering 75, no. 1 (2024): 154–59. http://dx.doi.org/10.54254/2755-2721/75/20240530.

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The evolution of object detection from traditional machine learning approaches to advanced deep learning techniques marks a significant milestone in the field of computer vision. Initially, object detection relied on algorithms such as Support Vector Machines (SVMs) and decision trees, leveraging handcrafted features like Histogram of Oriented Gradients (HOG) and Scale-Invariant Feature Transform (SIFT) for classification and recognition tasks. However, these methods exhibited limitations in scalability and adaptability to complex environments. The breakthrough came with the adoption of Convol

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Cao, Siwei. "Analysis of object recognition trends based on deep learning." Applied and Computational Engineering 5, no. 1 (2023): 292–99. http://dx.doi.org/10.54254/2755-2721/5/20230582.

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With the increasing development and maturity of deep learning, computers have also made world-renowned achievements in the domain of vision, especially in the basic and core branch of object detection, giving birth to many classical algorithms, which are widely used in many fields such as autonomous driving, intelligent medical care, intelligent security, and search entertainment. Before the emergence of deep learning algorithms, traditional algorithms for object detection were usually divided into three stages: region selection, feature extraction, and feature classification. However, with th

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Salemdeeb, M., and S. Erturk. "Multi-national and Multi-language License Plate Detection using Convolutional Neural Networks." Engineering, Technology & Applied Science Research 10, no. 4 (2020): 5979–85. https://doi.org/10.5281/zenodo.4016176.

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Many real-life machine and computer vision applications are focusing on object detection and recognition. In recent years, deep learning-based approaches gained increasing interest due to their high accuracy levels. License Plate (LP) detection and classification have been studied extensively over the last decades. However, more accurate and language-independent approaches are still required. This paper presents a new approach to detect LPs and recognize their country, language, and layout. Furthermore, a new LP dataset for both multinational and multi-language detection, with either one-line

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Ali, Wajihi, and Okouma Nguia. "Deep Learning Approaches for Fire Detection and Localization: A Vision-Based Review." International Journal of Multidisciplinary Research and Growth Evaluation. 6, no. 2 (2025): 77–82. https://doi.org/10.54660/.ijmrge.2025.6.2.77-82.

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Fire hazards pose a significant risk to human lives, infrastructure, and the environment, necessitating the development of efficient fire detection and localization systems. Traditional methods, including smoke and heat sensors, suffer from high false alarm rates and delayed response times. In recent years, deep learning has emerged as a transformative approach, leveraging computer vision to enhance accuracy in fire detection and localization. This paper provides a comprehensive survey of deep learning techniques employed for fire detection, including Convolutional Neural Networks (CNNs), obje

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Orozco-Arias, Simon, Luis Humberto Lopez-Murillo, Johan S. Piña, et al. "Genomic object detection: An improved approach for transposable elements detection and classification using convolutional neural networks." PLOS ONE 18, no. 9 (2023): e0291925. http://dx.doi.org/10.1371/journal.pone.0291925.

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Analysis of eukaryotic genomes requires the detection and classification of transposable elements (TEs), a crucial but complex and time-consuming task. To improve the performance of tools that accomplish these tasks, Machine Learning approaches (ML) that leverage computer resources, such as GPUs (Graphical Processing Unit) and multiple CPU (Central Processing Unit) cores, have been adopted. However, until now, the use of ML techniques has mostly been limited to classification of TEs. Herein, a detection-classification strategy (named YORO) based on convolutional neural networks is adapted from

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TS, Prof Nishchitha. "Real Time Object Detection in Autonomous Vehicle Using Yolo V8." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48914.

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Abstract Autonomous vehicles rely heavily on real-time object detection to ensure safe and efficient navigation in dynamic environments. This paper explores the implementation of YOLOv8 (You Only Look Once, version 8), a state-of-the-art deep learning model for object detection, within autonomous driving systems. YOLOv8 offers enhanced speed, accuracy, and lightweight deployment capabilities compared to its predecessors, making it highly suitable for real-time applications. The model is trained and evaluated on datasets such as KITTI and COCO to detect and classify various objects including pe

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Li, Chengran, Ajit Narayanan, and Akbar Ghobakhlou. "Overlapping Shoeprint Detection by Edge Detection and Deep Learning." Journal of Imaging 10, no. 8 (2024): 186. http://dx.doi.org/10.3390/jimaging10080186.

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In the field of 2-D image processing and computer vision, accurately detecting and segmenting objects in scenarios where they overlap or are obscured remains a challenge. This difficulty is worse in the analysis of shoeprints used in forensic investigations because they are embedded in noisy environments such as the ground and can be indistinct. Traditional convolutional neural networks (CNNs), despite their success in various image analysis tasks, struggle with accurately delineating overlapping objects due to the complexity of segmenting intertwined textures and boundaries against a backgrou

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Thomas3, Deepa Mariam. "Innovations in Wildfire and Smoke Detection: A Comprehensive Survey." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41825.

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Wildfires are a growing threat due to climate change, causing significant damage to ecosystems, property, and human lives. Effective detection systems are critical for prompt intervention and damage mitigation. This survey explores advances in fire and smoke detection, emphasizing the role of machine learning and computer vision techniques such as Convolutional Neural Networks (CNNs) and YOLO object detection models. Recent approaches leverage multi-source data, including satellite imagery, drone feeds, and ground sensors, to enhance detection accuracy and scalability. Key contributions includ

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Golande, Rushikesh, Rushikesh Bhapkar, Awantika Nalawade, Akash Rashinkar, and Shriganesh Mane. "Weapon Detection System: Real-Time Object Recognition for Threat Detection." International Journal for Research in Applied Science and Engineering Technology 13, no. 3 (2025): 3514–21. https://doi.org/10.22214/ijraset.2025.68105.

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Abstract: The increasing threat to public safety has driven the need for intelligent surveillance systems capable of detecting potential dangers in real time. This study introduces a Weapon Detection System (WDS) that utilizes advanced deep learning and computer vision techniques to identify firearms and other hazardous weapons in public areas. The system employs Convolutional Neural Networks (CNNs) and the YOLO (You Only Look Once) object detection model to ensure high accuracy and minimal latency in identifying threats from live video streams or images. Designed for real-time deployment, the

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Sree, S. Renuka. "NEW ERA OF VISION TO ENVISION USING YOLO." International Scientific Journal of Engineering and Management 03, no. 03 (2024): 1–9. http://dx.doi.org/10.55041/isjem01424.

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We provide a new approach to crowd counting in this research that makes use of the You Only Look Once (YOLO) algorithm. We show how well YOLO performs in precisely identifying and counting people in congested environments. Our method seeks to overcome the difficulties associated with crowd observation and analysis in real time. Urban planning, public safety, and event management are just a few of the many areas where crowd counting is important and dynamic. Occlusions, size variations, and congested settings are only a few of the challenges that traditional approaches frequently face in provid

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Gokila Deepa G. "An Enhanced Violence Detection Using Convolution Neural Networks Approach." Journal of Information Systems Engineering and Management 10, no. 40s (2025): 214–29. https://doi.org/10.52783/jisem.v10i40s.7265.

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Violence detecting through cameras is to find specific object identification and human action is prominent in office sectors and public places to enhance safety. CCTV images contain different objects, different backgrounds, real-time incident in office sectors and public places, in the existing methods residual network and K-nearest neighbors’ method were used to classify and detect violence. The disadvantages of these methods are finding objects and human behavior. The accuracy level is also the most complex to achieve. Therefore, in the proposed method, violence detection and object identifi

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Host, Kristina, Miran Pobar, and Marina Ivasic-Kos. "Analysis of Movement and Activities of Handball Players Using Deep Neural Networks." Journal of Imaging 9, no. 4 (2023): 80. http://dx.doi.org/10.3390/jimaging9040080.

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This paper focuses on image and video content analysis of handball scenes and applying deep learning methods for detecting and tracking the players and recognizing their activities. Handball is a team sport of two teams played indoors with the ball with well-defined goals and rules. The game is dynamic, with fourteen players moving quickly throughout the field in different directions, changing positions and roles from defensive to offensive, and performing different techniques and actions. Such dynamic team sports present challenging and demanding scenarios for both the object detector and the

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Khandade, Nandini. "Click and Cart Fashion Classification and Object Detection." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 2005–12. https://doi.org/10.22214/ijraset.2025.68667.

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In the rapidly evolving e-commerce industry, fashion classification and object detection play pivotal roles in enhancing user experience and improving operational efficiency. "Click & Cart" is an advanced system designed to address challenges in fashion retail by combining state-of-the-art computer vision techniques for object detection with machine learning algorithms for fashion classification. The system leverages deep learning models, particularly Convolutional Neural Networks (CNNs), to classify and detect fashion items such as clothing, accessories, and footwear from product images.

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Rao, P. Srinivas. "Road Surface Guard: AI Paved Safety." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 07, no. 12 (2023): 1–17. http://dx.doi.org/10.55041/ijsrem27709.

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Pothole detection is a critical aspect of road maintenance and safety, with the potential to prevent accidents and reduce infrastructure repair costs. Early detection and timely repair of potholes can help prevent accidents and reduce maintenance costs. Deep learning techniques have shown success in several computer vision tasks, including object detection and segmentation. The proposed system leverages Convolutional Neural Networks (CNNs), You Only Look Once (YOLO) object detection algorithm and Light Detection and Ranging (LiDAR) technology to identify and locate potholes in real-time. The s

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Dr., Manjot Kaur Sidhu. "Optimizing YOLOv3 with TensorFlow for Accurate and Efficient Object Detection." International Journal of Inventive Engineering and Sciences (IJIES) 12, no. 4 (2025): 20–26. https://doi.org/10.35940/ijies.E4624.12040425.

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<strong>Abstract:</strong> Object detection is a critical task in computer vision, with applications spanning autonomous driving, surveillance, and robotics. In this study, we implemented and evaluated the YOLOv3 model for real-time object detection. The model was tested on various images, demonstrating its ability to accurately detect and classify multiple objects with high confidence. The results indicate that YOLOv3 achieves a mean Average Precision (mAP) of 55–60% on the COCO dataset, aligning with its original performance benchmarks. Additionally, the model operates at an inference

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Swathi, Patakamudi, Dara Sai Tejaswi, Mohammad Amanulla Khan, Miriyala Saishree, Venu Babu Rachapudi, and Dinesh Kumar Anguraj. "Real-Time Vehicle Detection for Traffic Monitoring: A Deep Learning Approach." Data and Metadata 3 (April 13, 2024): 295. http://dx.doi.org/10.56294/dm2024295.

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Vehicle detection is an essential technology for intelligent transportation systems and autonomous vehicles. Reliable real-time detection allows for traffic monitoring, safety enhancements and navigation aids. However, vehicle detection is a challenging computer vision task, especially in complex urban settings. Traditional methods using hand-crafted features like HAAR cascades have limitations. Recent deep learning advances have enabled convolutional neural networks (CNNs) like Faster R-CNN, SSD and YOLO to be applied to vehicle detection with significantly improved accuracy. But each techniq

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Liwen Chen, Muhammad Shoaib Akram, Aafaq Saleem, and Hidayat Ullah. "Automatic Street Lighting System with Vehicle Detection using Deep-Learning Based Remote Sensing." International Journal of Engineering and Management Research 12, no. 2 (2022): 1–7. http://dx.doi.org/10.31033/ijemr.12.2.1.

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Automated street lightning is advantageous to society as it decreases the rate of accidents, vandalism and street crimes. The ability to detect vehicles and smartly manage the street light system is among the major duties of electrical distribution companies. To recognize an objects of interest in an image by a classical technique called object detection. In order to recover the enactment and reduce the complexity of object detection, numerous computer vision methods have been proposed over the past decade years. Object detection has a wide variety of applications including vehicle detection e

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Azurmendi, Iker, Ekaitz Zulueta, Jose Manuel Lopez-Guede, Jon Azkarate, and Manuel González. "Cooktop Sensing Based on a YOLO Object Detection Algorithm." Sensors 23, no. 5 (2023): 2780. http://dx.doi.org/10.3390/s23052780.

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Deep Learning (DL) has provided a significant breakthrough in many areas of research and industry. The development of Convolutional Neural Networks (CNNs) has enabled the improvement of computer vision-based techniques, making the information gathered from cameras more useful. For this reason, recently, studies have been carried out on the use of image-based DL in some areas of people’s daily life. In this paper, an object detection-based algorithm is proposed to modify and improve the user experience in relation to the use of cooking appliances. The algorithm can sense common kitchen objects

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Padmanabula, Sai Shilpa, Ramya Chowdary Puvvada, Venkatramaphanikumar Sistla, and Venkata Krishna Kishore Kolli. "Object Detection Using Stacked YOLOv3." Ingénierie des systèmes d information 25, no. 5 (2020): 691–97. http://dx.doi.org/10.18280/isi.250517.

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Object detection is a stimulating task in the applications of computer vision. It is gaining a lot of attention in many real-time applications such as detection of number plates of suspect cars, identifying trespassers under surveillance areas, detecting unmasked faces in security gates during the COVID-19 period, etc. Region-based Convolution Neural Networks(R-CNN), You only Look once (YOLO) based CNNs, etc., comes under Deep Learning approaches. In this proposed work, an improved stacked Yolov3 model is designed for the detection of objects by bounding boxes. Hyperparameters are tuned to get

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Bista, Rabindra, Anurag Timilsina, Anish Manandhar, et al. "Advancing Tuberculosis Detection in Chest X-rays: A YOLOv7-Based Approach." Information 14, no. 12 (2023): 655. http://dx.doi.org/10.3390/info14120655.

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In this work, we propose a CAD (computer-aided diagnosis) system using advanced deep-learning models and computer vision techniques that can improve diagnostic accuracy and reduce transmission risks using the YOLOv7 (You Only Look Once, version 7) object detection architecture. The proposed system is capable of accurate object detection, which provides a bounding box denoting the area in the X-rays that shows some possibility of TB (tuberculosis). The system makes use of CNNs (Convolutional Neural Networks) and YOLO models for the detection of the consolidation of cavitary patterns of the lesi

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Bello, Rotimi-Williams, Roseline Oluwaseun Ogundokun, Pius A. Owolawi, Etienne A. van Wyk, and Chunling Tu. "Application of Convolutional Neural Networks in Animal Husbandry: A Review." Mathematics 13, no. 12 (2025): 1906. https://doi.org/10.3390/math13121906.

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Convolutional neural networks (CNNs) and their application in animal husbandry have in-depth mathematical expressions, which usually revolve around how well they map input data such as images or video frames of animals to meaningful outputs like health status, behavior class, and identification. Likewise, computer vision and deep learning models are driven by CNNs to act intelligently in improving productivity and animal management for sustainable animal husbandry. In animal husbandry, CNNs play a vital role in the management and monitoring of livestock’s health and productivity due to their h

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Pallala Shreya, G. Sai Lohith Reddy2, Sevakula Sai Rathan, and R. Kanchana. "A Deep Learning Model for Crime Intention Analysis." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 05 (2025): 2117–21. https://doi.org/10.47392/irjaem.2025.0334.

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The “A Deep Learning Model for Crime Intention Analysis” project presents an intelligent surveillance system designed to enhance public safety through real-time video analysis. Traditional surveillance systems often suffer from delayed response times and heavy reliance on manual monitoring, leading to missed threats and inefficiencies. To overcome these limitations, the proposed model integrates deep learning and computer vision to automatically detect and classify suspicious human behaviors in various public and private environments such as airports, banks, and educational institutions. The s

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Gomaa, Ahmed, and Ahmad Abdalrazik. "Novel Deep Learning Domain Adaptation Approach for Object Detection Using Semi-Self Building Dataset and Modified YOLOv4." World Electric Vehicle Journal 15, no. 6 (2024): 255. http://dx.doi.org/10.3390/wevj15060255.

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Moving object detection is a vital research area that plays an essential role in intelligent transportation systems (ITSs) and various applications in computer vision. Recently, researchers have utilized convolutional neural networks (CNNs) to develop new techniques in object detection and recognition. However, with the increasing number of machine learning strategies used for object detection, there has been a growing need for large datasets with accurate ground truth used for the training, usually demanding their manual labeling. Moreover, most of these deep strategies are supervised and onl

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Ghatol, Ashwini. "Moving Vehicle Number Plate Detection." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 587–90. http://dx.doi.org/10.22214/ijraset.2024.61613.

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Abstract: The ability to detect and recognize vehicle license plates is a crucial task in various applications such as traffic management, law enforcement, and parking systems. In this project, we propose a system for moving vehicle license plate detection using image and video processing techniques. The system employs a combination of computer vision algorithms and machine learning models to accurately locate and recognize license plates from moving vehicles in real-time. The input images or video frames are preprocesses to enhance the regions containing license plates and reduce noise. This

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Patel, Krishna, Chintan Bhatt, and Pier Luigi Mazzeo. "Deep Learning-Based Automatic Detection of Ships: An Experimental Study Using Satellite Images." Journal of Imaging 8, no. 7 (2022): 182. http://dx.doi.org/10.3390/jimaging8070182.

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The remote sensing surveillance of maritime areas represents an essential task for both security and environmental reasons. Recently, learning strategies belonging to the field of machine learning (ML) have become a niche of interest for the community of remote sensing. Specifically, a major challenge is the automatic classification of ships from satellite imagery, which is needed for traffic surveillance systems, the protection of illegal fisheries, control systems of oil discharge, and the monitoring of sea pollution. Deep learning (DL) is a branch of ML that has emerged in the last few year

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Akhtar, Muhammad Bilal. "The Use of a Convolutional Neural Network in Detecting Soldering Faults from a Printed Circuit Board Assembly." HighTech and Innovation Journal 3, no. 1 (2022): 1–14. http://dx.doi.org/10.28991/hij-2022-03-01-01.

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Automatic Optical Inspection (AOI) is any method of detecting defects during a Printed Circuit Board (PCB) manufacturing process. Early AOI methods were based on classic image processing algorithms using a reference PCB. The traditional methods require very complex and inflexible preprocessing stages. With recent advances in the field of deep learning, especially Convolutional Neural Networks (CNN), automating various computer vision tasks has been established. Limited research has been carried out in the past on using CNN for AOI. The present systems are inflexible and require a lot of prepro

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Mohammed, Habib, Sekhra Salma, Tannouche Adil, and Ounejjar Youssef. "Enhancement of YOLOv5 for automatic weed detection through backbone optimization." IAES International Journal of Artificial Intelligence (IJ-AI) 14, no. 1 (2025): 658–66. https://doi.org/10.11591/ijai.v14.i1.pp658-666.

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In the context of our research project, which involves developing a robotic system capable of eliminating weeds using deep learning technics, the selection of powerful object detection model is essential. Object detectors typically consist of three components: backbone, neck, and prediction head. In this study, we propose an enhancement to the you only look once version 5 (YOLOv5) network by using the most popular convolutional neural networks (CNN) networks (such as DarkNet and MobileNet) as backbones. The objective of this study is to identify the best backbone that can improve YOLOv5 's per

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Hithaishi Surendra, Et al. "Lane Detection and Traffic Sign Detection using Deep Learning and Computer Vision for Autonomous Driving Research Using CARLA Simulator." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 10 (2023): 2062–69. http://dx.doi.org/10.17762/ijritcc.v11i10.8891.

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Lane identification and traffic sign detection is the most challenging and promising problem for self-driving or autonomous vehicles with unintentional lane departure and ignorance of traffic signs being major contributing factors to motor vehicle collisions around the world. To tackle this problem the proposed work aims to detect both lane and traffic signs for autonomous vehicles. This article proposes semantic segmentation and object detection model for implementing Advanced Driver Assistance System (ADAS) applications. The applications are implemented using a variant of Convolutional Neura

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FAHMI, C. "Rescue Vision: AI Insight for Emergencies." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48934.

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Abstract— To improve safety procedures in a variety of locations, such as workplaces, public areas, and industrial sites, the project aims to create a comprehensive visual surveillance system that integrates fire detection with density counts of people. The system uses computer vision, deep learning, and artificial intelligence (AI) to analyze surveillance camera footage in order to identify fire dangers and count people in real time. It also provides dual alarms to increase fire safety and security. Even under difficult circumstances with congestion or obstacles, the system recognizes human f

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Rao, Kolukula Nitalaksheswara, Kalapala Rajendra Prasad, Rao Ivaturi Sundara Siva, Tammineni Ravi Kumar, Mahalakshmi Annavarapu, and Uma Pyla. "An efficient object detection by autonomous vehicle using deep learning." An efficient object detection by autonomous vehicle using deep learning 14, no. 4 (2024): 4287–95. https://doi.org/10.11591/ijece.v14i4.pp4287-4295.

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The automation industries have been developing since the first demonstration in the period 1980 to 2000 it is mainly used on automated driving vehicle. Now a day’s automotive companies, technology companies, government bodies, research institutions and academia, investors and venture capitalists are interested in autonomous vehicles. In this work, object detection on road is proposed, which uses deep learning (DL) algorithms. You only look once (YOLO V3, V4, V5). In this system object detection on the road data set is taken as input and the objects are

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