The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
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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In the citing paper
“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
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803
YOLOv3: An Incremental Improvement
Redmon, J., & Farhadi, A. (2018) · arXiv · doi:10.48550/arxiv.1804.02767 · Cited by 17,141
YOLOv4: Optimal Speed and Accuracy of Object Detection
Bochkovskiy, A., Wang, C.-Y., & Liao, H.-Y. M. (2020) · arXiv · doi:10.48550/arxiv.2004.10934 · Cited by 10,307
A Guide to Convolutional Neural Networks for Computer Vision
Khan, S. H., Rahmani, H., Shah, S. A. A., & Bennamoun, M. (2018) · Synthesis lectures on computer vision · doi:10.2200/s00822ed1v01y201712cov015 · Cited by 727
Feature Extraction and Image Recognition with Convolutional Neural Networks
Liu, Y. H. (2018) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1087/6/062032 · Cited by 181
Weapon Detection using Artificial Intelligence and Deep Learning for Security Applications
Jain, H., Vikram, A., Mohana, Kashyap, A., & Jain, A. (2020) · International Conference on Electronics and Sustainable Communication Systems (ICESC) · doi:10.1109/icesc48915.2020.9155832 · Cited by 124
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
Franklin, R. J., & Mohana (2020) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces48766.2020.9137873 · Cited by 100
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
Self-citationNehashree, M. R., Pallavi Raj, S., & Mohana (2019) · International Conference on Intelligent Sustainable Systems (ICISS) · doi:10.1109/iss1.2019.8907990 · Cited by 42
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Real-Life Violence Detection in Sports Videos Using MoViNet
Zachariah, L., & Maharajan, K. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-27368-0_5
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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