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.
“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]
Each YOLO iteration features optimized backbones that contribute to faster processing times and better detection capabilities.
“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]
Significant advancements in YOLO 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.
“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]
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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 · see citing works
Integrated 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 · see citing works
Deep 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 · see citing works
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 · see citing works
SIF Tracking: Novel Estimation Filter for Object Detection and Tracking
Moksyakov, A. (2021) · The Atrium (University of Guelph)
Monitor Learning for Virtual Classroom
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Blind Assistance System using Digital Image ProcessingPreprint
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Detection and Comparative Results of Plant Diseases Based on Deep Learning
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Detection of Criminal Situations for Surveillance in Commercial Establishments
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- 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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