Relevant bibliographies by topics / Real-time objects detection

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
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Academic literature on the topic 'Real-time objects detection'

Author: Grafiati
Published: 5 June 2025
Last updated: 15 July 2025

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Journal articles on the topic "Real-time objects detection"

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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.

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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.
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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.

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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.
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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.

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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.
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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.

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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.
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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.

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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.
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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.

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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.
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Saranya, M., and S. Arulselvarani. "Real-Time Obstacle Detection using Yolov8 for Assistive Navigation." Indian Journal Of Science And Technology 18, no. 25 (2025): 2009–22. https://doi.org/10.17485/ijst/v18i25.937.

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Objectives: The study is designed to find obstacles immediately in a person’s field of view using stereo vision, deep learning, and computer vision methods. When the system calculates the distance and angle of every obstacle, the user can drive it safely through the environment. Distance and angle to nearby objects are mainly controlled because it improves awareness and security on the road. Methods: The system detects objects using YOLOv8 and tracks them with Deep SORT using a video from the camera. Taking observations from a series of known points, distance and position angles can be determined. The user can accurately and precisely store detailed information on any object using real-time data logging and retrieve it in graph and CSV format. Findings: The accuracy rate was 92.4%, the precision rate was 91.8% and the F1 score was 90.5%. Using a PC, it can track movement and instantly pass on accurate information about the things happening around a user. They use timestamps, several object categories, and several measures of distance, different angles, and types of detectors. Novelty: The proposal is a custom combination of YOLOv8, Deep SORT, and stereo vision in real-time obstacle detection and is optimized to suit the needs of visually impaired users. The system describes three key innovations as a formula of danger estimation that combines depth, object class, motion trajectory and angular position to output real-time risk scoring, a dynamic processing framework that can variable frame rate based on the object motion patterns to obtain a better balance between computation and motion processing, and a modular hardware-software synchronization approach that is designed to efficiently operate with low-power embedded systems. The system allows live CSV logging, timestamp-based performance visualization, and audio-haptic feedback, making it a complete assistive system. Keywords: Obstacle Detection, Stereo Vision, YOLOv8, Visual Impairment, Real-time Navigation
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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.

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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.
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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.

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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.
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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.

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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..
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Dissertations / Theses on the topic "Real-time objects detection"

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Olvång, Leif. "Real-time Collision Detection with Implicit Objects." Thesis, Uppsala University, Department of Information Technology, 2010. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-129453.

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<p>Collision detection is a problem that has been studied in many different contexts. Lately one of the most common context has been rigid multi-body physics for different types of simulations.</p><p>A popular base algorithm in this context is Gilbert-Johnson-Keerthi's algorithm for measuring the distance between two convex objects. This algorithm belongs to a family of algorithms which share the common property of allowing implicitly defined objects.</p><p>In this thesis we give a theoretical overview of the algorithms in this family and discuss things to keep in mind when implementing them. We also give a presentation of how they behave in different situations based on our experiments. Finally we give recommendations for in which general cases one should use which algorithm.</p>
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Kumara, Muthukudage Jayantha. "Automated Real-time Objects Detection in Colonoscopy Videos for Quality Measurements." Thesis, University of North Texas, 2013. https://digital.library.unt.edu/ark:/67531/metadc283843/.

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The effectiveness of colonoscopy depends on the quality of the inspection of the colon. There was no automated measurement method to evaluate the quality of the inspection. This thesis addresses this issue by investigating an automated post-procedure quality measurement technique and proposing a novel approach automatically deciding a percentage of stool areas in images of digitized colonoscopy video files. It involves the classification of image pixels based on their color features using a new method of planes on RGB (red, green and blue) color space. The limitation of post-procedure quality measurement is that quality measurements are available long after the procedure was done and the patient was released. A better approach is to inform any sub-optimal inspection immediately so that the endoscopist can improve the quality in real-time during the procedure. This thesis also proposes an extension to post-procedure method to detect stool, bite-block, and blood regions in real-time using color features in HSV color space. These three objects play a major role in quality measurements in colonoscopy. The proposed method partitions very large positive examples of each of these objects into a number of groups. These groups are formed by taking intersection of positive examples with a hyper plane. This hyper plane is named as 'positive plane'. 'Convex hulls' are used to model positive planes. Comparisons with traditional classifiers such as K-nearest neighbor (K-NN) and support vector machines (SVM) proves the soundness of the proposed method in terms of accuracy and speed that are critical in the targeted real-time quality measurement system.
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Karakas, Samet. "Detecting And Tracking Moving Objects With An Active Camera In Real Time." Master's thesis, METU, 2011. http://etd.lib.metu.edu.tr/upload/12613712/index.pdf.

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Moving object detection techniques can be divided into two categories based on the type of the camera which is either static or active. Methods of static cameras can detect moving objects according to the variable regions on the video frame. However, the same method is not suitable for active cameras. The task of moving object detection for active cameras generally needs more complex algorithms and unique solutions. The aim of this thesis work is real time detection and tracking of moving objects with an active camera. For this purpose, feature based algorithms are implemented due to the computational efficiency of these kinds of algorithms and SURF (Speeded Up Robust Features) is mainly used for these algorithms. An algorithm is developed in C++ environment and OpenCV library is frequently used. The developed algorithm is capable of detecting and tracking moving objects by using a PTZ (Pan-Tilt-Zoom) camera at a frame rate of approximately 5 fps and with a resolution of 640x480.
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Söderlund, Henrik. "Real-time Detection and Tracking of Moving Objects Using Deep Learning and Multi-threaded Kalman Filtering : A joint solution of 3D object detection and tracking for Autonomous Driving." Thesis, Umeå universitet, Institutionen för tillämpad fysik och elektronik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:umu:diva-160180.

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Perception for autonomous drive systems is the most essential function for safe and reliable driving. LiDAR sensors can be used for perception and are vying for being crowned as an essential element in this task. In this thesis, we present a novel real-time solution for detection and tracking of moving objects which utilizes deep learning based 3D object detection. Moreover, we present a joint solution which utilizes the predictability of Kalman Filters to infer object properties and semantics to the object detection algorithm, resulting in a closed loop of object detection and object tracking.On one hand, we present YOLO++, a 3D object detection network on point clouds only. A network that expands YOLOv3, the latest contribution to standard real-time object detection for three-channel images. Our object detection solution is fast. It processes images at 20 frames per second. Our experiments on the KITTI benchmark suite show that we achieve state-of-the-art efficiency but with a mediocre accuracy for car detection, which is comparable to the result of Tiny-YOLOv3 on the COCO dataset. The main advantage with YOLO++ is that it allows for fast detection of objects with rotated bounding boxes, something which Tiny-YOLOv3 can not do. YOLO++ also performs regression of the bounding box in all directions, allowing for 3D bounding boxes to be extracted from a bird's eye view perspective. On the other hand, we present a Multi-threaded Object Tracking (MTKF) solution for multiple object tracking. Each unique observation is associated to a thread with a novel concurrent data association process. Each of the threads contain an Extended Kalman Filter that is used for predicting and estimating an associated object's state over time. Furthermore, a LiDAR odometry algorithm was used to obtain absolute information about the movement of objects, since the movement of objects are inherently relative to the sensor perceiving them. We obtain 33 state updates per second with an equal amount of threads to the number of cores in our main workstation.Even if the joint solution has not been tested on a system with enough computational power, it is ready for deployment. Using YOLO++ in combination with MTKF, our real-time constraint of 10 frames per second is satisfied by a large margin. Finally, we show that our system can take advantage of the predicted semantic information from the Kalman Filters in order to enhance the inference process in our object detection architecture.
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Hinterstoißer, Stefan Verfasser], Nassir [Akademischer Betreuer] [Navab, Bernt [Akademischer Betreuer] Schiele, and Kurt [Akademischer Betreuer] Konolige. "Real-time detection and pose estimation of low-textured and texture-less objects / Stefan Hinterstoißer. Gutachter: Bernt Schiele ; Kurt Konolige. Betreuer: Nassir Navab." München : Universitätsbibliothek der TU München, 2012. http://d-nb.info/1030099480/34.

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Konstantinidis, Michalis. "Preimplantation genetic diagnosis : new methods for the detection of genetic abnormalities in human preimplantation embryos." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:28611f65-7729-4293-9c3f-4fc3f0cc39d7.

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Preimplantation genetic diagnosis (PGD) refers to the testing of embryos produced through in vitro fertilization (IVF) in order to identify those unaffected by a specific genetic disorder or chromosomal abnormality. In this study, different methodologies were examined and developed for performance of PGD. Investigation of various whole genome amplification (WGA) methods identified multiple displacement amplification as a reliable method for genotyping single cells. Furthermore, this technology was shown to be compatible with subsequent analysis using single nucleotide polymorphism (SNP) microarrays. Compared to conventional methods used in this study to perform single cell diagnosis (e.g. multiplex PCR), WGA techniques were found to be advantageous since they streamline the development of PGD protocols for couples at high risk of transmitting an inherited disorder and simultaneously offer the possibility of comprehensive chromosome screening (CCS). This study also aimed to develop a widely applicable protocol for accurate typing of the human leukocyte antigen (HLA) region with the purpose of identifying embryos that will be HLA-identical to an existing sibling affected by a disorder that requires haematopoietic stem cell transplantation. Additionally, a novel microarray platform was developed that, apart from accurate CCS, was capable of reliably determining the relative quantity of mitochondrial DNA in polar bodies removed from oocytes and single cells biopsied from embryos. Mitochondria are known to play an important role in oogenesis and preimplantation embryogenesis and their measurement may therefore be of clinical relevance. Moreover, real-time PCR was used for development of protocols for CCS, DNA fingerprinting of sperm samples and embryos and the relative quantitation of telomere length in embryos (since shortened telomeres might be associated with reduced viability). As well as considering the role of genetics in terms of oocyte and embryo viability assessment and the diagnosis of inherited genetic disorders, attention was given to a specific gene (Phospholipase C zeta) of relevance to male infertility. A novel mutation affecting the function of the resulting protein was discovered highlighting the growing importance of DNA sequence variants in the diagnosis and treatment of infertility.
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Morris, Gruffydd Beaufoy. "Autonomous real-time object detection and identification." Thesis, Lancaster University, 2017. http://eprints.lancs.ac.uk/88485/.

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Sensor devices are regularly used on unmanned aerial vehicles (UAVs) as reconnaissance and intelligence gathering systems and as support for front line troops on operations. This platform provides a wealth of sensor data and has limited computational power available for processing. The objective of this work is to detect and identify objects in real-time, with a low power footprint so that it can operate on a UAV. An appraisal of current computer vision methods is presented, with reference to their performance and applicability to the objectives. Experimentation with real-time methods of background subtraction and motion estimation was carried out and limitations of each method described. A new, assumption free, data driven method for object detection and identification was developed. The core ideas of the development were based on models that propose that the human vision system analyses edges of objects to detect and separate them and perceives motion separately, a function which has been modelled here by optical flow. The initial development in the temporal domain combined object and motion detection in the analysis process. This approach was found to have limitations. The second iteration used a detection component in the spatial domain that extracts texture patches based on edge contours, their profile, and internal texture structure. Motion perception was performed separately on the texture patches using optical flow. The motion and spatial location of texture patches was used to define physical objects. A clustering method is used on the rich feature set extracted by the detection method to characterise the objects. The results show that the method carries out detection and identification of both moving and static objects, in real-time, irrespective of camera motion.
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Gunnarsson, Adam. "Real time object detection on a Raspberry Pi." Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-89573.

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With the recent advancement of deep learning, the performance of object detection techniques has greatly increased in both speed and accuracy. This has made it possible to run highly accurate object detection with real time speed on modern desktop computer systems. Recently, there has been a growing interest in developing smaller and faster deep neural network architectures suited for embedded devices. This thesis explores the suitability of running object detection on the Raspberry Pi 3, a popular embedded computer board. Two controlled experiments are conducted where two state of the art object detection models SSD and YOLO are tested in how they perform in accuracy and speed. The results show that the SSD model slightly outperforms YOLO in both speed and accuracy, but with the low processing power that the current generation of Raspberry Pi has to offer, none of the two performs well enough to be viable in applications where high speed is necessary.
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Chen, Meihong. "Real-Time Video Object Detection with Temporal Feature Aggregation." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42790.

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In recent years, various high-performance networks have been proposed for single-image object detection. An obvious choice is to design a video detection network based on state-of-the-art single-image detectors. However, video object detection is still challenging due to the lower quality of individual frames in a video, and hence the need to include temporal information for high-quality detection results. In this thesis, we design a novel interleaved architecture combining a 2D convolutional network and a 3D temporal network. We utilize Yolov3 as the base detector. To explore inter-frame information, we propose feature aggregation based on a temporal network. Our temporal network utilizes Appearance-preserving 3D convolution (AP3D) for extracting aligned features in the temporal dimension. Our multi-scale detector and multi-scale temporal network communicate at each scale and also across scales. The number of inputs of our temporal network can be either 4, 8, or 16 frames in this thesis and correspondingly we name our temporal network TemporalNet-4, TemporalNet-8 and TemporalNet-16. Our approach achieves 77.1\% mAP (mean Average Precision) on ImageNet VID 2017 dataset with TemporalNet-4, where TemporalNet-16 achieves 80.9\% mAP which is a competitive result on this video object detection benchmark. Our network is also real-time with a running time of 35ms/frame.
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Tivelius, Malcolm. "Real-time Small Object Detection using Deep Neural Networks." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-300060.

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Object detection is a research area within computer vision that consists of both localising and classifying objects in images. The applications of this kind of research in society are many, ranging from facial recognition to self driving cars. Some of these use cases requires the detection of objects in motion and are therefore considered to be in a separate category of object detection, commonly referred to as real time object detection. The goal of this thesis is to shed further light on the area of real time object detection by investigating the effectiveness of successful object detection techniques when applied to objects of smaller sizes. More specifically, the task of detecting small objects is described by the community as a difficult problem. This is also an area that has not been extensively researched before and the results could thus be used by the research community at large and/or for real life applications. This paper is a comparative study between the effectiveness of two different deep learning techniques within real time object detection, namely RetinaNet and YOLOv3. The objects used are small characters and digits that are engraved onto ball bearings. Ball bearings have been photographed while traveling on a production line, and a collection of such images are what constitutes the dataset used in this study. The goal is to classify as many characters and digits as possible on each bearing, with as low inference time as possible. The two deep learning models were implemented and then evaluated on their performance, measured in terms of precision and average inference time. The evaluation was performed on labeled bearings not previously seen by the two models. The results showthat RetinaNet vastly outperformsYOLOv3 when it comes to real-time object detection of small objects in terms of mAP@50. However, when it comes to average inference time YOLOv3 performed twice as fast as RetinaNet. In conclusion it can be noted that YOLOv3 struggles when it comes to smaller objects whereas RetinaNet excels in this area. It can also be concluded, from previous research, that an increase in mAP and average inference time is most likely limited by the hardware used during training. The verification of this could be a potential further investigation of this thesis<br>Objektdetektering är ett forskningsområde inom datorseende som går ut på att både lokalisera och klassificera objekt i bilder. Användingsområdena för den här typen av forskning är många och innefattar allt från ansiktsigenkänning till självkörande bilar. En del av användningsområdena kräver att man kan detektera objekt som är i rörelse. De här fallen ingår i ett separat forskningsområde som är känt som realtids-objektdetektering. Målet med den här studien är att belysa det här forsningsområdet ytterligare, genom att undersöka hur tidigare lyckade objektdetekteringsmodeller presterar när det kommer till små objekt. Det är ett område inom datorseende som inte har studerats extensivt tidigare. Vidare beskrivs detektering av små objekt generellt som ett svårt problem av forskningssamhället. Resultaten av den här studien skulle därför både kunna användas av andra forskare och tillämpas i praktiken för problem med samma konfigurationer. Den här studien är en jämförande studie mellan prestandan på två olika djupinlärningstekniker inom realtids-objektdetektering, som heter RetinaNet och YOLOv3. Objekten som har studerats för att besvara forskningsfrågan är små bokstäver och siffror som är inprintade på sidan av kullager. Kullagerna har fotats i en fabrik när de åker på en produktionslina, och en sammanställning av dessa fotografier är vad som utgör det dataset som använts för att träna och utvärdera modellerna. Målet är att klassificera så många av bokstäverna och siffrorna som möjligt, på en så kort tid som möjligt. De två djupinlärningsteknikerna har implementerats och deras prestanda har utvärderats, mätt i precision samt genomsnittlig inferenstid. Utvärderingen skedde på bilder med utmarkerade rätta svar som ingen av modellerna hade sett tidigare. Resultaten visar att RetinaNet presterar avsevärt bättre än YOLOv3 när det kommer till realtids-objektdetektering av små objekt med hänsyn till mAP@50. Dock, när det kommer till genomsnittlig inferenstid så är YOLOv3 dubbelt så snabb som RetinaNet. Slutsatsen kan dras att YOLOv3 har svårt att detektera små objekt, medan RetinaNet är relativt bra lämpad för det. Det kan också konkluderas att en ökning av prestanda mest troligt är begränsad av hårdvaran som använts under träning av modellerna. Det kan vara av intresse att vidare utforska det här i framtiden.
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Books on the topic "Real-time objects detection"

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Datta, Asit K., Bijaya Ketan Panigrahi, Siddhartha Bhattacharyya, Nilanjan Dey, and Ajoy Kumar Ray. Real-Time Visual Object Detection and Tracking. Elsevier Science & Technology Books, 2021.

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Book chapters on the topic "Real-time objects detection"

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Niitsuma, Hiroaki, and Tsutomu Maruyama. "Real-Time Detection of Moving Objects." In Field Programmable Logic and Application. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30117-2_154.

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Sharma, Pallavi, Isha Malhotra, Palak Handa, and Nidhi Goel. "Real-Time Detection of Household Objects Using Single-Shot Detection With MobileNet." In Communications in Computer and Information Science. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-91340-2_9.

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Liu, Chunfang, Jiali Fang, and Pan Yu. "Real-Time Visual Detection of Anomalies in Densely Placed Objects." In Cognitive Computation and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-0885-7_6.

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Qi, Baojun, Tao Wu, Hangen He, and Tingbo Hu. "Real-Time Detection of Small Surface Objects Using Weather Effects." In Computer Vision – ACCV 2010. Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19318-7_3.

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Staudenrausch, Tim, and Bernd Lüdemann-Ravit. "Cycle Time Measurement Using AI-Based Object Detection and Tracking in Industrial Processes." In ARENA2036. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-88831-1_22.

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Abstract This paper presents an AI-based system for improving cycle time measurement in industrial environments, leveraging YOLOv8 for object detection and ByteTrack for tracking. Our non-invasive approach analyzes video from an Azure Kinect camera to calculate cycle times by detecting objects and monitoring their state changes. Tested at the University of Applied Sciences Kempten’s demo plant, the system showcased high accuracy against ground truth data, highlighting its potential to enhance production line monitoring and efficiency significantly. This work contributes to industrial automation by offering a real-time, accurate method for cycle time analysis, promising substantial advancements in manufacturing process optimization.
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Guan, Weiguang, and Patricia Monger. "Real-Time Detection of Out-of-Plane Objects in Stereo Vision." In Advances in Visual Computing. Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11919476_11.

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Solunke, Babruvan R., and Sachin R. Gengaje. "Real-Time Multi-objects Detection Using YOLOv7 for Advanced Driving Assistant Systems." In Lecture Notes in Networks and Systems. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-3466-5_9.

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Indukuri, Hemalatha, K. Kishore Raju, P. KavyaSri, M. Srija, K. Srujana, and P. SivaPriya. "Blind People Assistant: Real-Time Objects Detection and Distance Estimation with Voice Feedback." In Algorithms in Advanced Artificial Intelligence. CRC Press, 2024. http://dx.doi.org/10.1201/9781003529231-37.

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Hu, Yaxuan, Yuehong Dai, and Zhongxiang Wang. "Real-time Detection of Tiny Objects Based on a Weighted Bi-directional FPN." In MultiMedia Modeling. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-98358-1_1.

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Sharma, Hrishikesh, Tanima Dutta, V. Adithya, and P. Balamuralidhar. "A Real-Time Framework for Detection of Long Linear Infrastructural Objects in Aerial Imagery." In Lecture Notes in Computer Science. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-20801-5_8.

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Conference papers on the topic "Real-time objects detection"

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Zhilenkov, Anton A., and Oleg S. Krupinin. "Real-Time Motion Detection of Flying Objects With Dynamic Camera." In 2025 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM). IEEE, 2025. https://doi.org/10.1109/icieam65163.2025.11028163.

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Liao, Teh-Lu, Chien-Chun Liu, Chia-Ming Lu, Ying-Chih Lai, Yi-You Hou, and Yang-Tin Lou. "Implementation of Multi-Fields Real-Time Image Detection of Small Hazardous Objects." In 2024 IEEE 13th Global Conference on Consumer Electronics (GCCE). IEEE, 2024. https://doi.org/10.1109/gcce62371.2024.10760257.

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S, Kanagamalliga, Latha R, Sugitha N, Iraianbu E, Guru S, and Renugadevi R. "Real-Time Detection of Road Objects and Lane Markings for Autonomous Vehicles." In 2025 5th International Conference on Trends in Material Science and Inventive Materials (ICTMIM). IEEE, 2025. https://doi.org/10.1109/ictmim65579.2025.10988008.

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Ayman, Shehab Eldeen, Walid Hussein, and Omar H. Karam. "Depth-Based Region Proposal: Multi-Stage Real-Time Object Detection." In 12th International Conference on Digital Image Processing and Vision. Academy & Industry Research Collaboration, 2023. http://dx.doi.org/10.5121/csit.2023.131305.

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Many real-time object recognition systems operate on two-dimensional images, degrading the influence of the involved objects' third-dimensional (i.e., depth) information. The depth information of a captured scene provides a thorough understanding of an object in fulldimensional space. During the last decade, several region proposal techniques have been integrated into object detection. scenes’ objects are then localized and classified but only in a two-dimensional space. Such techniques exist under the umbrella of two-dimensional object detection models such as YOLO and SSD. However, these techniques have the issue of being uncertain that an object's boundaries are properly specified in the scene. This paper proposes a unique region proposal and object detection strategy based on retrieving depth information for localization and segmentation of the scenes’ objects in a real-time manner. The obtained results on different datasets show superior accuracy in comparison to the commonly implemented techniques with regards to not only detection but also a pixel-by-pixel accurate localization of objects.
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Hag Ahmed, Yousif, Wenyao Li, and Sohel Anwar. "Real-Time Detection of Negative Road Objects for Autonomous Vehicles to Improve Safety." In ASME 2024 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2024. https://doi.org/10.1115/imece2024-144674.

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Abstract This paper presents the preliminary results of our investigation in detecting negative road objects such as potholes and cracks in real-time for autonomous vehicles in order to improve safety. It is important to not only detect the existence of the negative road object from a safe distance, but also to identify the size, depth, and shape of such objects which plays a critical role in determining if the object must be avoided for safety or it can be ignored. We utilized open-source image databases such as ROBOFLOW (pothole) and RDD2020 of negative road objects for training a single layer convolutional neural network (CNN), such as YOLO. To test the trained algorithm, a small data set taken on the city streets in Indianapolis, IN was used in order to evaluate the accuracy of detection of the negative road objects. While the initial results show the ability of detecting the negative road objects through the proposed algorithm, more work needs to be done to improve the overall accuracy of detection with respect to size and shape of these negative road objects.
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Cheng, Harry H., Benjamin D. Shaw, Joe Palen, Jonathan E. Larson, Xudong Hu, and Kirk Van Katwyk. "A Real-Time Laser-Based Detection System for Measurement of Delineations of Moving Vehicles." In ASME 1999 Design Engineering Technical Conferences. American Society of Mechanical Engineers, 1999. http://dx.doi.org/10.1115/detc99/cie-9072.

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Abstract In current practice, quantitative traffic data are most commonly acquired from inductive loops. In addition, video-image processing or time-of-flight laser systems can be used. These methods all have problems associated with them. We have developed a non-intrusive laser-based detection system for measurement of vehicle travel time. The basic detector unit consists of a fan angle laser and a photodetector array positioned above the plane of detection. This detection system is able to determine the length and width of moving objects in real time with high resolution. This information is used to differentiate similar objects and can be used later for re-identification of individual objects or object groups, providing a real measure of travel time between detection sites.
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Wixson, Lambert E., and Dana H. Ballard. "Real-Time Detection Of Multi-Colored Objects." In 1989 Symposium on Visual Communications, Image Processing, and Intelligent Robotics Systems, edited by Paul S. Schenker. SPIE, 1990. http://dx.doi.org/10.1117/12.969997.

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Raheja, Jagdish Lal, Chaitanya Malireddy, Aniket Singh, and L. Solanki. "Detection of abandoned objects in real time." In 2011 3rd International Conference on Electronics Computer Technology (ICECT). IEEE, 2011. http://dx.doi.org/10.1109/icectech.2011.5941684.

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Joseph, Ejiyi Chukwuebuka, Olusola Bamisile, Nneji Ugochi, Qin Zhen, Ndalahwa Ilakoze, and Chikwendu Ijeoma. "Systematic Advancement of Yolo Object Detector For Real-Time Detection of Objects." In 2021 18th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). IEEE, 2021. http://dx.doi.org/10.1109/iccwamtip53232.2021.9674163.

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O'Neill, Kevin, Fridon Shubitidze, and Benjamin E. Barrowes. "EMI real-time subsurface target location by analytical dHP." In Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXIII, edited by Jason C. Isaacs and Steven S. Bishop. SPIE, 2018. http://dx.doi.org/10.1117/12.2305177.

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Reports on the topic "Real-time objects detection"

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Keller, Mareike, Aaron J. Becker, Nikolaj Diller, et al. Monitoring ecological consequences of marine munition in the Baltic Sea 2024 - Cruise No. AL622, 14th – 21st October 2024, Kiel (Germany) – Kiel (Germany), „POST-Clear“. GEOMAR Helmholtz Centre for Ocean Research Kiel, Germany, 2024. https://doi.org/10.3289/cr_al622.

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ALKOR cruise AL622 took place as part of the project CONMAR (https://conmarmunition.eu/) which is part of the DAM mission sustainMare (https://www.sustainmare.de/). It was the continuation of the munition monitoring started within the BMBF‐funded project UDEMM (Environmental Monitoring for the Delaboration of Munition in the Sea; https://udemm.geomar.de/), the EMFF (European Maritime and Fisheries Fund) ‐funded projects BASTA (Boost Applied munition detection through Smart data detection in and AI workflows; https://www.basta‐munition.eu) and ExPloTect (Ex‐situ, near‐real‐time detection compound detection in seawater). ALKOR worked for one week in the Baltic Sea in the munition dumpsites Kolberger Heide, Lübeck Bight and the military training area of Schönhagen. Munition sites were mapped via hydroacoustic (subbottom profiler and synthetic aperture sonar) and visual (AUV, ROV and towed camera) methods. Water samples were taken for explosive-type compounds analysis and sediment samples for macro faunal distribution studies. Beam-trawl fishing was done for analyzing explosive-type compounds in local fish species and mussel moorings next to munition objects are part of an environmental monitoring. A change of crew happened on the 16th and 18th October in Neustadt i.H. with support of the munition clearance company SeaTerra. (Alkor-Berichte ; AL622)
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Greinert, Jens. Mine Monitoring in the German Baltic Sea 2020; Dumped munition monitoring AL548, 03rd – 16th November 2020, Kiel (Germany) – Kiel (Germany) „MineMoni-II 2020“. GEOMAR Helmholtz Centre for Ocean Research Kiel, 2021. http://dx.doi.org/10.3289/cr_al548.

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ALKOR cruise AL548 took place as part of the EMFF (European Maritime and Fisheries Fund)-funded project BASTA (Boost Applied munition detection through Smart data inTegration and AI workflows; https://www.basta-munition.eu) and as continuation of the munition monitoring started within the BMBF-funded project UDEMM (Environmental Monitoring for the Delaboration of Munition in the Sea; https://udemm.geomar.de/). In October 2018, a first cruise (POS530 MineMoni2018) was conducted, to gather data for a broad baseline study in the German Baltic Sea. Results show a moderate contamination level on regional and coastal scale, but indicate higher levels for specific local areas. Within UDEMM, expertise was developed to detect, exactly locate and monitor munition (e.g. torpedoes, sea mines, ground mines) on the seafloor using optical and hydroacoustic means. In addition, chemical analyses of dissolved contaminants in the water and sediments was performed. Data acquired during this cruise are used in BASTA, which aims for enhanced munition detection via AUV-based artificial intelligence applied on multi-sensor datasets. At the same time, the project ExPloTect (Ex-situ, near-real-time exPlosive compound deTection in seawater) (also EMFF-funded) addresses the need for an innovative approach to detect explosive compounds in seawater. A prototype system was used and successfully tested for the first time during this cruise. The main focus was placed onto the two already known dumpsites Kolberger Heide and Lübeck Bight. Additionally, new areas Falshöft (Schleswig-Holstein) and Cadet Channel, Trollegrund and Großklützhöved (Mecklenburg-Vorpommern) were explored. In each area high-resolution multibeam mapping was performed and contact lists, indicating potential munition objects were produced on board. AUV surveys were conducted to ground-truth possible contacts via detailed photograph and magnetometer mapping. This was complemented with towed video (TV)-CTD profiles. The transits to and between those sites were planned along former constraint routes during WWII. These routes were main targets of the British Air Force and mines and bombs can be expected along these ways. During transits water samples were taken with on a CTD- (conductivity, temperature, depth) rosette-mounted Niskin bottles in regular distances, in order to obtain a comprehensive understanding munition compounds (inter alia trinitrotoluene (TNT)) measurements across the German Baltic Sea.
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Chambers, D., D. Paglieroni, J. Mast, and N. Beer. Real-Time Vehicle-Mounted Multistatic Ground Penetrating Radar Imaging System for Buried Object Detection. Office of Scientific and Technical Information (OSTI), 2013. http://dx.doi.org/10.2172/1068301.

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Motorny, Sergey, S. Glandon, and Jing-Ru Cheng. The design of multimedia object detection pipelines within the HPC environment. Engineer Research and Development Center (U.S.), 2025. https://doi.org/10.21079/11681/49599.

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Computer vision multimedia pipelines have become both more sophisticated and robust over the years. The pipelines can accept multiple inputs, perform frame analysis, and produce outputs on a variety of platforms with near-real-time performance. Vendors such as Nvidia have significantly grown their framework and library offerings while providing tutorials and documentation via online training and tutorials. Despite the prolific growth, many of the libraries, frameworks, and tutorials come with noticeable limitations. The limitations are especially apparent within the high-performance computing (HPC) environment where graphic processing units may be older, user-level rights more restricted, and access to the graphical user interface not always available. This work describes the process of building multimedia object detection and segmentation pipelines within the HPC environment, its challenges, and ways to overcome the shortcomings. The project describes an iterative design process, which can be used as a blueprint for future development of similar computer vision pipelines within the HPC hosting environment.
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Guan, Jiajing, Sophia Bragdon, and Jay Clausen. Predicting soil moisture content using Physics-Informed Neural Networks (PINNs). Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/48794.

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Environmental conditions such as the near-surface soil moisture content are valuable information in object detection problems. However, such information is generally unobtainable at the necessary scale without active sensing. Richards’ equation is a partial differential equation (PDE) that describes the infiltration process of unsaturated soil. Solving the Richards’ equation yields information about the volumetric soil moisture content, hydraulic conductivity, and capillary pressure head. However, Richards’ equation is difficult to approximate due to its nonlinearity. Numerical solvers such as finite difference method (FDM) and finite element method (FEM) are conventional in approximating solutions to Richards’ equation. But such numerical solvers are time-consuming when used in real-time. Physics-informed neural networks (PINNs) are neural networks relying on physical equations in approximating solutions. Once trained, these networks can output approximations in a speedy manner. Thus, PINNs have attracted massive attention in the numerical PDE community. This project aims to apply PINNs to the Richards’ equation to predict underground soil moisture content under known precipitation data.
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Panta, Manisha, Padam Thapa, Md Hoque, et al. Application of deep learning for segmenting seepages in levee systems. Engineer Research and Development Center (U.S.), 2024. http://dx.doi.org/10.21079/11681/49453.

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Seepage is a typical hydraulic factor that can initiate the breaching process in a levee system. If not identified and treated on time, seepages can be a severe problem for levees, weakening the levee structure and eventually leading to collapse. Therefore, it is essential always to be vigilant with regular monitoring procedures to identify seepages throughout these levee systems and perform adequate repairs to limit potential threats from unforeseen levee failures. This paper introduces a fully convolutional neural network to identify and segment seepage from the image in levee systems. To the best of our knowledge, this is the first work in this domain. Applying deep learning techniques for semantic segmentation tasks in real-world scenarios has its own challenges, especially the difficulty for models to effectively learn from complex backgrounds while focusing on simpler objects of interest. This challenge is particularly evident in the task of detecting seepages in levee systems, where the fault is relatively simple compared to the complex and varied background. We addressed this problem by introducing negative images and a controlled transfer learning approach for semantic segmentation for accurate seepage segmentation in levee systems.
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