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YOLOv3 and YOLOv4: Multiple Object Detection for Surveillance Applications
Abstract
Object detection algorithm such as You Only Look Once (YOLOv3 and YOLOv4) is implemented for traffic and surveillance applications. A neural network consists of input with minimum one hidden and output layer. Multiple object dataset (KITTI image and video), which consists of classes of images such as Car, truck, person, and two-wheeler captured during RGB and grayscale images. The dataset is composed (image and video) of varying illumination. YOLO model variants such as YOLOv3 is implemented for image and YOLOv4 for video dataset. Obtained results show that the algorithm effectively detects the objects approximately with an accuracy of 98% for image dataset and 99% for video dataset.
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“Since its inception, the YOLO architecture has undergone continuous evolution, with each version from YOLOv1 through to the latest YOLOv9 introducing enhancements in accuracy, speed, and efficiency [37].”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [37]
In the citing paper
“Additionally, each YOLO iteration features optimized backbones that contribute to faster processing times and better detection capabilities.”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [37]
In the citing paper
“Significant advancements include the introduction of anchor boxes, which improve the model's ability to detect objects of varying shapes and sizes, as well as new loss functions aimed at optimizing performance.”
From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [37]
Citations
Cites
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Self-citationOptimized YOLOv3 Algorithm and Its Application in Traffic Flow Detections
Huang, Y., Zheng, J., Sun, S., Yang, C., & Liu, J. (2020) · Applied Sciences · doi:10.3390/app10093079 · Cited by 115
Traffic Signal Violation Detection using Artificial Intelligence and Deep Learning
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Self-citationClassification of Objects in Video Records using Neural Network Framework
Biswas, A., Jana, A. P., Mohana, & Tejas, S. S. (2018) · International Conference on Smart Systems and Inventive Technology (ICSSIT) · doi:10.1109/icssit.2018.8748560 · Cited by 44
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Self-citation
Cited by
Sorted by citations
Ali, M. L., & Zhang, Z. (2024) · Computers · doi:10.3390/computers13120336 · Cited by 383
“Table 3 provides a comparison of the strengths and weaknesses of various YOLO variants, showcasing the tradeoffs between speed, accuracy, and complexity.”
T-YOLO: Tiny Vehicle Detection Based on YOLO and Multi-Scale Convolutional Neural Networks
Padilla Carrasco, D., Rashwan, H. A., García, M. Á., & Puig, D. (2023) · IEEE Access · doi:10.1109/access.2021.3137638 · Cited by 145
“The first section, the backbone (i.e., called cspdarknet [22]), is composed, in the case of Yolo-v5, of the most common operations in CNNs (e.g., convolutions, concatenations, max-pooling) and a simple forwarding mechanism that is constructed to extract multiple features for the next section.”
BackgroundPradhyumna, P., Shreya, G., & Mohana (2021) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc51422.2021.9532631 · Cited by 93
Self-citationQuantum Convolutional Neural Networks (QCNN) Using Deep Learning for Computer Vision Applications
Rajesh, V., Naik, U. P., & Mohana (2021) · International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) · doi:10.1109/rteict52294.2021.9574030 · Cited by 77
“Though QCNN uses architecture analogous to CNN, it uses qubits to develop a neural network between the stacked layers of the CNN architecture [34] [35] [36].”
MethodSelf-citationSwin transformer based vehicle detection in undisciplined traffic environment
Deshmukh, P., Satyanarayana, G. S. R., Majhi, S., Sahoo, U. K., & Das, S. K. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118992 · Cited by 63
Deep Learning based Object Detection Model for Autonomous Driving Research using CARLA Simulator
Niranjan, D. M. K., VinayKarthik, B. C., & Mohana (2021) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec51865.2021.9591747 · Cited by 52
“The object detection algorithm used was YOLO v2 while an ROS node was used to assess the distance between the vehicles in an image [41] [42].”
MethodSelf-citationAshwin Shenoy, M., & Thillaiarasu, N. (2022) · International Conference on Computing Methodologies and Communication (ICCMC) · doi:10.1109/iccmc53470.2022.9753931 · Cited by 47
Retracted3D Object Detection and Tracking Methods using Deep Learning for Computer Vision Applications
Shreyas, E., Sheth, M. H., & Mohana (2021) · International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) · doi:10.1109/rteict52294.2021.9573964 · Cited by 39
“A. Gupta [4] discuss comprehensive methods that address image perception issues and assess recently developed frameworks and tests conducted on self-driving cars using deep learning perspectives [27] [28] [29].”
BackgroundSelf-citationRohith, M., Sunil, A., & Mohana (2021) · International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT) · doi:10.1109/rteict52294.2021.9573996 · Cited by 38
“Used scalable object detection and tracking of moving objects using YOLO method [28] [33].”
MethodSelf-citationSunil, A., Sheth, M. H., Shreyas, E., & Mohana (2021) · International Conference on Electrical, Computer and Communication Technologies (ICECCT) · doi:10.1109/icecct52121.2021.9616791 · Cited by 36
“It is used to detect multiple classes in a single frame and is also faster than other SOTA detectors [27] [28].”
MethodSelf-citationA Deep Learning Method for Foot Progression Angle Detection in Plantar Pressure Images
Ardhianto, P., Subiakto, R. B. R., Lin, C., Jan, Y., Liau, B.-Y., Tsai, J.-Y., Akbari, V. B. H., & Lung, C.-W. (2022) · Sensors · doi:10.3390/s22072786 · Cited by 31
Zheng, L., Chen, Y., Yan, L., & Zhang, Y. (2023) · International Journal of Architectural Heritage · doi:10.1080/15583058.2023.2246029 · Cited by 24
Naik, U. P., Rajesh, V., R, R. K., & Mohana (2021) · 2021 Fourth International Conference on Electrical, Computer and Communication Technologies (ICECCT) · doi:10.1109/icecct52121.2021.9616625 · Cited by 23
Self-citationPita, M. S. U., Alon, A. S., Melo, P. M. B., Hernandez, R., & Magboo, A. I. (2021) · IEEE Student Conference on Research and Development (SCOReD) · doi:10.1109/scored53546.2021.9652769 · Cited by 22
Shenoy, M. A., & Thillaiarasu, N. (2023) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-233919 · Cited by 21
“AConvolutional Neural Network with prior training (CNN) built on the You Only Look Once (YOLO) architecture [62, 63] is used to validate the detection and classification of activities.”
Detection of Animals in Thermal Imagery for Surveillance using GAN and Object Detection Framework
Khatri, K., Asha, C. S., & D’Souza, J. M. (2022) · International Conference for Advancement in Technology (ICONAT) · doi:10.1109/iconat53423.2022.9725883 · Cited by 22
Catalysis of neural activation functions: Adaptive feed-forward training for big data applications
Sarkar, S., Agrawal, S., Baker, T., Maddikunta, P. K. R., & Gadekallu, T. R. (2022) · Applied Intelligence · doi:10.1007/s10489-021-03082-y · Cited by 19
“Convolution Neural Networks (CNN) are being widely applied today in several big data applications of radiology [11], face and object detection [12], document analysis [13] etc.”
BackgroundMethodDetecting the Early Flowering Stage of Tea Chrysanthemum Using the F-YOLO Model
Chao, Q., Nyalala, I., & Chen, K. (2021) · Agronomy · doi:10.3390/agronomy11050834 · Cited by 18
“For the backbone component, the main network is CSP-DenseNet [32]; in addition, a CBL (convolution + batch normalization + leaky ReLU) [33] module and an SPP addon module are added to the component.”
MethodYOLO for early detection and management of Tuta absoluta-induced tomato leaf diseases
Shehu, H. A., ACKLEY, A. U., Mark, M., Eteng, O. E., Sharif, M. H., & Kusetoğulları, H. (2025) · Frontiers in Plant Science · doi:10.3389/fpls.2025.1524630 · Cited by 22
Niranjan, D. M. K., VinayKarthik, B. C., & Mohana (2021) · International Conference on Electrical, Computer and Communication Technologies (ICECCT) · doi:10.1109/icecct52121.2021.9616712 · Cited by 17
Self-citationChernyshеv, D., Dolhopolov, S., Honcharenko, T., Haman, H., Ivanova, T., & Zinchenko, M. (2022) · IEEE International Conference on Computer Sciences and Information Technologies (CSIT) · doi:10.1109/csit56902.2022.10000717 · Cited by 15
“As part of the object search, the system performs several stages related to data acquisition, image selection, data separation, training, training, and model output [11] – [13].”
A Framework to Estimate the Key Point Within an Object Based on a Deep Learning Object Detection
Kurdthongmee, W., Suwannarat, K., & Wattanapanich, C. (2023) · HighTech and Innovation Journal · doi:10.28991/hij-2023-04-01-08 · Cited by 15
Performance evaluation of salient object detection techniques
Ahmed, K., Gad, M. A., & Aboutabl, A. E. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12567-y · Cited by 16
“FCN models is the one-stage object detection models such as OverFeat [145], You Only Look Once (YOLO) [146], YOLO V2 [147], YOLO V3 [148, 149] and edge guidance network (EGNet) [150].”
MethodAn Optimization-Based Technology Applied for Face Skin Symptom Detection
Liao, Y., Chang, P.-C., Wang, C.-C., & Li, H.-H. (2022) · Healthcare · doi:10.3390/healthcare10122396 · Cited by 14
Comprehensive study on object detection for security and surveillance: A concise review
Alamri, F. (2025) · Multimedia Tools and Applications · doi:10.1007/s11042-025-20801-6 · Cited by 14
“Similarly, YOLOv3 and YOLOv4 [54] have been implemented for traffic and surveillance systems.”
Towards a Platform for Smart City-Scale Cognitive Assistance Applications
Rausch, T., Hummer, W., Stippel, C., Vasiljevic, S., Elvezio, C., Dustdar, S., & Krösl, K. (2021) · IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW) · doi:10.1109/vrw52623.2021.00066 · Cited by 14
“We invoke the API 400 times with a 416x416 pixel image, which is the common input size for YOLO.”
MethodInfluentialOn the Importance of Backbone to the Adversarial Robustness of Object Detectors
Li, X., Chen, H., & Hu, X. (2025) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2025.3542964 · Cited by 18
“Object detection is a fundamental task in computer vision and has plenty of security-critical real-world applications, such as autonomous driving [15], video surveillance [16], and face recognition [17], [18], [19], [20].”
Real-Time Image Based Weapon Detection Using YOLO Algorithms
Gali, M., Dhavale, S., & Kumar, S. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-12641-3_15 · Cited by 13
Target Recognition and Detection in Side-Scan Sonar Images based on YOLO v3 Model
Li, J., & Cao, X. S. (2022) · Chinese Control Conference (CCC) · doi:10.23919/ccc55666.2022.9902742 · Cited by 12
“The convolutional neural network and the YOLOv3 model are used to perform one-time neural network processing on the submarine side-scan sonar image [11,12], and the Darknet53 network is used as the backbone extraction network.”
MethodPerformance Analysis of YOLO Algorithms for Real-Time Crowd Counting
Ranjan, A., Pathare, N., Dhavale, S., & Kumar, S. (2022) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon55314.2022.9909018 · Cited by 12
“Kumar et al., [15] presented an algorithm to detect vehicle.”
MethodA Novel Deep Learning-Enabled Physical Education Mechanism
Wang, W., & Jiang, J. (2022) · Mobile Information Systems · doi:10.1155/2022/8455164 · Cited by 11
“*e research of paper [23, 24] is based on real-time multiple object detection through YOLO.”
BackgroundWang, H., Chaw, J. K., Goh, S. K., Shi, L., Tin, T. T., Huang, N., & Gan, H. (2024) · IEEE Access · doi:10.1109/access.2024.3442484 · Cited by 12
“It is 7.61% and 4.79% ahead of YOLOv3 [32] and YOLOv5-NAM [33], respectively, but since our backbone is based on the self-attention module, it results in a slower runtime than the former two.”
BackgroundInfluentialAn Efficient Safety and Authorized Helmet Detection Using Deep Learning Approach
Susa, J. A. B., Maaliw, R. R., III, Ceribo, C. M. C., Macalisang, J. R., & Fabro, B. C. (2022) · IEEE International Conference on Smart Information Systems and Technologies (SIST) · doi:10.1109/sist54437.2022.9945729 · Cited by 10
Centralised and Decentralised Sensor Fusion-Based Emergency Brake Assist
Deo, A., Palade, V., & Huda, M. N. (2021) · Sensors · doi:10.3390/s21165422 · Cited by 10
“algorithm divides the image into numerous smaller regions and predicts probabilities of object presence, and its bounding box within the selected region [37].”
BackgroundInfluentialMachine-Learning Object Detection and Recognition for Surveillance System using YoloV3
Soma, S., & Waddenkery, N. (2022) · International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT) · doi:10.1109/iceeict53079.2022.9768558 · Cited by 9
Venugopalan, H., Din, Z. A., Carpenter, T., Lowe-Power, J., King, S. T., & Shafiq, Z. (2023) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3631406 · Cited by 12
“…sanitizer since YOLO-based models have demonstrated fast and accurate predictions [27, 52, 59– 61] on diverse objects (vehicles, animals, furniture, electronics etc [20, 25, 45]) in various environments (aerial images [77], microscopic images [46], images of moving objects in traffic [40, 69] etc).”
BackgroundAn Effective Smart Goggle to Address the Problem Faced by the Visually Impaired People
Munot, A. V., Mohod, E. N., Hemnani, S. R., Narkhede, K., & Sonare, B. (2023) · International Conference for Emerging Technology (INCET) · doi:10.1109/incet57972.2023.10169909 · Cited by 8
“It provides Intelligent multiple object detection using image and video but they found that it lacks in prioritybased detection [4].”
BackgroundScale-aware token-matching for transformer-based object detector
Jung, A., Hong, S., & Hyun, Y. (2024) · Pattern Recognition Letters · doi:10.1016/j.patrec.2024.08.006 · Cited by 8
Design of CanSat for Environmental Monitoring and Object Detection
Hasan, M., Rahman, I. I., Hossam-E-Haider, M., & Sadman, A. A. M. S. (2021) · International Conference on Electrical Engineering and Information Communication Technology · doi:10.1109/iceeict53905.2021.9667830 · Cited by 8
Adaptive Traffic Signal Management Using Real-Time Vehicle Detection and Tracking
Shirulkar, S., Makode, R., & Khandelwal, R. (2025) · IEEE International Conference on Interdisciplinary Approaches in Technology and Management for Social Innovation (IATMSI) · doi:10.1109/iatmsi64286.2025.10984828 · Cited by 7
A Review on Advanced Detection Methods in Vehicle Traffic Scenes
Chan, M. N., & Tint, T. (2021) · International Conference on Inventive Computation Technologies (ICICT) · doi:10.1109/icict50816.2021.9358791 · Cited by 8
“In [20], there is implemented multiple object detection algorithms, YOLOv3 and YOLOv4 for traffic and surveillance applications.”
BackgroundMethodInfluentialDetection and Classification Knee Osteoarthritis Algorithm using YOLOv3 and VGG-16 Models
Huu, P. N., Thành, Đ. N., Hai, T. L. T., Duc, H. C., Viet, H. P., & Trong, C. N. (2022) · International Conference on Applying New Technology in Green Buildings (ATiGB) · doi:10.1109/atigb56486.2022.9984096 · Cited by 7
“YOLOv4 [9], [10] archived the state-of-the-art with many strengths compared to other YOLO models because of speed and performance.”
BackgroundMurthy, A. K., Prathwini, & Kulkarni, S. (2024) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc60852.2024.10689830 · Cited by 6
Hard-hat Detection using YOLOv4
Bhadeshiya, R. N., Brahmbhatt, K., & Pitroda, J. (2021) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc51422.2021.9532896 · Cited by 6
“It also captures photos and videos of building sites to offer a wealth of information about them [1] [2].”
BackgroundReview on Vision Based Human Motion Detection Using Deep Learning
Zachariah, L., & Maharajan, K. (2023) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec58147.2023.10276103 · Cited by 6
Comparative Study of Various Crowd Detection and Classification Methods for Safety Control System
Khan, N., & Haroon, M. (2022) · International Journal of Engineering and Management Research · doi:10.2139/ssrn.4146666 · Cited by 6
Fu, L., Zhang, Q., & Tian, S. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e26467 · Cited by 8
“[19], the performance of two models, YOLOv3 and YOLOv4, is compared for vehicle detection in highway surveillance videos.”
MethodTraffic Signs Detection and Recognition System Using the YOLOv4 Algorithm
Arief, R. W., Nurtanio, I., & Samman, F. A. (2021) · IEEE International Conference on Artificial Intelligence and Mechatronics Systems (AIMS) · doi:10.1109/aims52415.2021.9466006 · Cited by 5
Elango, S., & Ramachandran, N. (2021) · doi:10.1002/essoar.10508221.1 · Cited by 5
Deep Learning Based Object Detection in Surveillance Video
Arun, M. R., Bennet, M. A., Satav, S. D., Manikandan, V., Gopianand, M., & Dawande, N. A. (2023) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec58147.2023.10276262 · Cited by 4
“[1] Nowadays Artificial intelligence is getting more popular.”
BackgroundGlove Detection System on Laboratory Members Using Yolov4
Bari, A. K. A., Rachmawati, E., & Kosala, G. (2023) · Journal of Information System Research (JOSH) · doi:10.47065/josh.v4i4.3806 · Cited by 4
“After the detection stage, the detected objects are classified based on their class [14].”
BackgroundAugmented Home Security System with Computer Vision Based Human Activity Recognition
Thillaiarasu, N., Satish, P., Bhargava, V., Reddy, S. V., & Radhakrishnan, A. (2024) · International Conference on Advanced Computing and Communication Systems (ICACCS) · doi:10.1109/icaccs60874.2024.10716826 · Cited by 4
Pemantauan Physical Distance Pada Area Umum Menggunakan YOLO Tiny V3
Hasani, M. C., Milenasari, F., & Setyawan, N. (2022) · Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) · doi:10.29207/resti.v6i1.3808 · Cited by 4
Weapon Detection Using ML for PPA
Hashim, N., Dhas, D. A. S., & Melekoodappattu, J. G. (2022) · Advances in intelligent systems and computing · doi:10.1007/978-981-16-7330-6_61 · Cited by 4
Computer-Vision-Oriented Adaptive Sampling in Compressive Sensing
Liu, L., Nishikawa, H., Zhou, J., Taniguchi, I., & Onoye, T. (2024) · Sensors · doi:10.3390/s24134348 · Cited by 4
“For detection, we utilzied YOLOv3 [52], which has been well-established by other works such as [53–56] for inference using the KITTI dataset.”
MethodProposing Algorithm Using YOLOV4 and VGG-16 for Smart-Education
Huu, P. N., & Xuan, K. D. (2021) · Applied Computational Intelligence and Soft Computing · doi:10.1155/2021/1682395 · Cited by 4
An Improved and Efficient YOLOv4 Method for Object Detection in Video Streaming
Hussain, J., Prathap, B. R., & Sharma, A. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-2211-4_27 · Cited by 3
Intelligent Traffic Management System Using YOLO Machine Learning Model
Gomathi, B., & Ashwin, G. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-2177-3_12 · Cited by 3
Performance Comparison of Object Detection Neural Network Models Based on Accuracy and Latency
Gomathy, B., Sengottaiyan, N., Aarthi, K., Thirumoorthy, P., Tamizharasu., K., & Kalyanasundaram, P. (2024) · International Conference on Intelligent Data Communication Technologies and Internet of Things (IDCIoT) · doi:10.1109/idciot59759.2024.10467336 · Cited by 3
“The authors suggest utilizing the YOLOv4 algorithm for multiple object detection in surveillance applications [9].”
MethodReal Time Surveillance System Using Yolov8
Bharadwaja, D., Sri, G. B., Azeez, A., & Nikitha, K. (2024) · Information systems engineering and management · doi:10.1007/978-3-031-69197-3_9 · Cited by 3
"Just Drive": Colour Bias Mitigation for Semantic Segmentation in the Context of Urban Driving
Stelling, J., & Atapour–Abarghouei, A. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9672033 · Cited by 3
“decade, and their performance in computer vision tasks is state of the art [7], [8], [9].”
Saikrishnan, V., & Karthikeyan, M. (2023) · International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) · doi:10.1109/icscds56580.2023.10104740 · Cited by 3
Switching Trackers for Effective Sensor Fusion in Advanced Driver Assistance Systems
Deo, A., & Palade, V. (2022) · Electronics · doi:10.3390/electronics11213586 · Cited by 2
Person Detection and Tracking Using UAV and Neural Networks
Stan, A.-S., Ichim, L., Parvu, V.-P., & Popescu, D. (2023) · Mediterranean Conference on Control and Automation (MED) · doi:10.1109/med59994.2023.10185718 · Cited by 2
“Each of the predicted boxes is linked by the loss function to the image's ground truth boxes [10].”
BackgroundForensic Analysis of Drones Attacker Detection Using Deep Learning
Editya, A. S., Kurniati, N., Alamin, M. M., Pramana, A. L., & Lisdiyanto, A. (2024) · Scientific Journal of Informatics · doi:10.15294/sji.v11i1.48183 · Cited by 2
Development and validation of a real-time vision-based automatic HDMI wire-split inspection system
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“Although each network has its characteristics, the YOLO series detection networks have been widely used in many practicalreal-timedetectionapplications[28–33].”
Automatic Vacant Parking Places Management System Using Multicamera Vehicle Detection
Computer Science and Engineering (Internet Of Things) Guru Nanak Institutions Technical Campus Telangana, I., Veena, B. K., Sachin, M., Kumar, S. A., & Surekha, B. (2025) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem40550 · Cited by 2
Fozi, N. M., Saifudin, S. A., Sulaiman, S. N., Riyadi, S., & Karim, N. K. A. (2025) · IEEE International Conference on Automatic Control and Intelligent Systems (I2CACIS) · doi:10.1109/i2cacis65476.2025.11101077 · Cited by 2
Comparison Using Intelligent Systems for Data Prediction and Near Miss Detection Techniques
Lim, L. M., Sathasivam, S., Ismail, M. T., Mohamed, A. S. A., Ibidoja, O. J., & Ali, M. K. M. (2023) · Pertanika journal of science & technology · doi:10.47836/pjst.32.1.20 · Cited by 2
Ivanova, B., Shoilekova, K., & Rusev, R. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-53549-9_11 · Cited by 2
Face mask detection utilizing “You only look one (YOLOV3)” for Covid-19 response
Fikih, M. A., Putri, T. S. N. P., Kasan, N., & Setyawan, N. (2022) · AIP conference proceedings · doi:10.1063/5.0094468 · Cited by 2
Automatic Firearm Detection in Images and Videos Using YOLO-Based Model
Mishra, S., & Chaurasiya, V. K. (2023) · Communications in computer and information science · doi:10.1007/978-981-99-1648-1_46 · Cited by 1
A Deep Learning-Based Approach for Real-Time Object Detection and Recognition
Juyal, A. (2021) · Mathematical Statistician and Engineering Applications · doi:10.17762/msea.v70i2.2322 · Cited by 1
Character Recognition on Forged Entities using AI
Rao, K., Awale, R. N., Diwan, A., Rathod, S. M., & Sharma, A. K. (2022) · International Conference on Industry 4.0 Technology (I4Tech) · doi:10.1109/i4tech55392.2022.9952894 · Cited by 1
“In this implementation the YOLOv4 model has been used for detection and recognition of characters engraved on the metal parts [3-5].”
MethodYOLO v5 and Faster R-CNN Performance Evaluation of Solid Waste in Object Detection Application
Pradeep Kumar, T., Lilly Florence, M., & Fathima, G. S. (2023) · Advances in multimedia and interactive technologies book series · doi:10.4018/978-1-6684-6060-3.ch022 · Cited by 1
Pedestrian Tracking Algorithm Based on IRB-JDE
Qian, H., Xu, Z., & Wang, J. (2022) · China Automation Congress (CAC) · doi:10.1109/cac57257.2022.10054786 · Cited by 1
“For example, the improved Deep SORT algorithm based on YOLOv3 [7] and YOLOv4 [8] is proposed in [9] , which decreases the missing detection under the weak illumination and object occlusion.”
MethodYOLO Algorithms for Real-Time Fire Detection
Ranjan, A., Dhavale, S., & Kumar, S. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-99-1414-2_40 · Cited by 1
To Enhance Nighttime Vehicle Recognition with YOLO Technology Based on Optical and Thermal Images
Kuan, C.-W., Yeh, M.-L., Chou, T., & Chen, W.-Z. (2023) · International Journal of Latest Engineering Research and Applications (IJLERA) · doi:10.56581/ijlera.8.5.01-14 · Cited by 1
Detection of Violent Elements in Digital Games Using Deep Learning
Yalçın, N., & Çapanoğlu, A. E. (2023) · SN Computer Science · doi:10.1007/s42979-023-02064-w · Cited by 1
“The YoloV4-Tiny model uses a linear and non-monotonic Mish activation function [19].”
BackgroundMethodIntegrated and Deep Learning Based Social Surveillance System: A Novel Approach
Litoriya, R., Ramchandani, D., Moyal, D., & Bothra, D. (2023) · Journal of Automation Mobile Robotics & Intelligent Systems · doi:10.14313/jamris/3-2022/22 · Cited by 1
“[12] suggest that for traffic and surveillance applications, object detection algorithms such as You Only Look Once (YOLOv3 and YOLOv4) be used.”
MethodDeep learning based object detection and counting in tomato field
Priyanka, V., Sandhya, P., & Vani, V. C. (2024) · AIP conference proceedings · doi:10.1063/5.0182358 · Cited by 1
Proposed Congestion Warning System Using YOLOv8 and DeepSORT model
Huu, P. N., Quynh, P. N., Thanh, L. N., Dang, D. D., Hai, T. L. T., Nguyen, T. D., & Minh, Q. T. (2023) · RIVF International Conference on Computing and Communication Technologies (RIVF) · doi:10.1109/rivf60135.2023.10471851 · Cited by 1
“Anchor boxes are rectangles of predef ned size and scale, used in traditional object recognition models such as YOLOv3 [7].”
MethodSIF Tracking: Novel Estimation Filter for Object Detection and Tracking
Moksyakov, A. (2021) · The Atrium (University of Guelph)
Monitor Learning for Virtual Classroom
Dhakal, G., Bista, A., Gautam, K., Chhetri, N. G., & Thippeswamy, M. N. (2021) · IEEE Global Conference for Advancement in Technology (GCAT) · doi:10.1109/gcat52182.2021.9587691
Zhang, Q., Sun, H., & Wang, J. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-16-7156-2_28
Hybrid Approach for Detecting the Traffic Violations Based on Deep Learning Using the Real-Time Data
Gupta, P., Rajkumar, R., Santhanalakshmi, S., & Amudha, J. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-981-19-3035-5_66
Saikrishnan, V., & Karthikeyan, M. (2023) · International Journal of Science and Research (IJSR) · doi:10.21275/sr23929155916
Blind Assistance System using Digital Image ProcessingPreprint
Pooja, S., Prasanna Kumar, K., Madhu, B. R., & N, S. K. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4528448
Detection and Comparative Results of Plant Diseases Based on Deep Learning
Çakır, M. M., & Çınarer, G. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-50920-9_33
Audio Enabled Object Detection System with NLP based Interaction
Chakrapani, A. P. (2024) · INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT · doi:10.55041/ijsrem34865
“Obtained results show that the algorithm effectively detects the objects approximately with an accuracy of 98% for image dataset and 99% for video dataset [14].”
BackgroundTwo-Wheeler Classification using Deep Learning (YOLOv5)
Abudhagir, U. S., Koushik, K., Goud, G. B. K., Reddy, K. V., Karthik, K., & Sravanthi, B. (2024) · doi:10.1109/iccsp60870.2024.10543299
“In terms of accuracy, the YOLO V4 outperforms the V3 when compared [7].”
MethodRoad traffic target detection method based on improved YOLOv5
Zhou, H., & Xue, Y. (2024) · International Conference on Computer Engineering and Application (ICCEA) · doi:10.1109/iccea62105.2024.10603618
“…classical object detection methods can be divided into two categories, single-stage detectors: you only look once (YOLO) [3] , YOLOv3 [6] , YOLOv4 [13] , YOLOv5 [8] , YOLO9000 [5] , YOLOX [7] ,etc. Two-stage detectors: Faster R-CNN [1][4] ,SPP-Net [14] ,VFNet [15] , etc. Single-stage detection…”
YOLOv3 for Brain Tumor Detection
Chefrour, A., Drissi, S., Kechaichia, N., & Haou, H. (2024) · Lecture notes in networks and systems · doi:10.1007/978-3-031-60591-8_6
Detection of insulators defects based on YOLOv3 and YOLOv4
Lin, J.-J., & Chou, K.-H. (2024) · AIP conference proceedings · doi:10.1063/5.0235088
Grande-Barreto, J., López-Armas, G. D. C., Sanchez-Tiro, J. A., & Barreto, H. P. (2024) · Life · doi:10.3390/life14121634
YOLO-GCEF: A Novel Detection Algorithm for Timber Transport Trucks in Forest Farms
Wu, S., Wang, Z., Li, A., Bai, X., Fang, R., & Yin, G. (2024) · CAA International Conference on Vehicular Control and Intelligence (CVCI) · doi:10.1109/cvci63518.2024.10830041
Suspicious Object Monitoring System Based on Multiview Model from Smart-camera
Huu, P. N., Nam, K. N. H., Dang, D. D., Thi, T. N., Manh, H. T., Minh, Q. T., & Minh, Q. T. (2024) · International Conference on Advanced Technologies for Communications · doi:10.1109/atc63255.2024.10908264
“The authors [1], [2] proposed a framework that uses the YOLOv3 model to detect people and the Deepsort method to track detected people using bounding boxes and assigned ID information.”
The Application and Performance Comparison of Different Versions of YOLO Image Recognition Systems
Chen, Y., & Chen, Y.-X. (2025) · IEEE International Conference on Social Sciences and Intelligence Management (SSIM) · doi:10.3390/engproc2025098001 · Cited by 1
“For instance, in animal recognition [6], mineral classification [7], and inspection systems [8], the differences in accuracy and efficiency between these two models have been discussed to highlight technological advancements and applicability.”
Ghosh, A., Bhattacharya, S., Roy, S., Ray, S., Upadhyay, R., & Chakraborty, S. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-96-4008-9_43
Detection of Criminal Situations for Surveillance in Commercial Establishments
Sandoval-Robayo, E., Castro-Martin, A. P., & Escobar-Naranjo, J. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-031-98768-7_8
Metadata
- Authors
- Kumar B., C., Punitha, R., & Mohana
- Affiliations
- R.V. College of Engineering (IN)
- ORCID
- Mohana
- Venue
- 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT)
- Publisher
- IEEE
- Published
- 2020-08
- Pages
- 1316-1321
- OpenAlex
- W3092269081
- Cited by
- 101
- References
- 16
- Citations by year
- 2026: 2 · 2025: 8 · 2024: 21 · 2023: 21 · 2022: 26 · 2021: 23
- FWCI
- 5.40
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- Locations
- International Conference on Smart Systems and Inventive Technology (ICSSIT) (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Video Surveillance and Tracking Methods
- Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
- Keywords
- YOLOv3, YOLOv4, object detection, multiple object detection, video surveillance, traffic monitoring, KITTI dataset, car detection, human detection, grayscale images, illumination variation
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