Optimizing Real-Time Object Detection- A Comparison of YOLO Models
Keywords:
Weapon Detection; YOLO models; Security; Deep Learning; Learning RateAbstract
Gun and weapon détection plays a crucial role in security, surveillance, and law enforcement. This study conducts a comprehensive comparison of all available YOLO (You Only Look Once) models for their effectiveness in weapon detection. We train YOLOv1, YOLOv2, YOLOv3, YOLOv4, YOLOv5, YOLOv6, YOLOv7, and YOLOv8 on a custom dataset of 16,000 images containing guns, knives, and heavy weapons. Each model is evaluated on a validation set of 1,400 images, with mAP (mean average precision) as the primary performance metric. This extensive comparative analysis identifies the best performing YOLO variant for gun and weapon detection, providing valuable insights into the strengths and weaknesses of each model for this specific task.
References
Vijayakumar and S. Vairavasundaram, "YOLO-based Object Detection Models: A Review and its Applications," Multimedia Tools and Applications, pp. 1-40, 2024.
P. Azevedo and V. Santos, "Comparative analysis of multiple YOLO-based target detectors and trackers for ADAS in edge devices," Robotics and Autonomous Systems, vol. 171, p. 104558, 2024.
M. Hussain, "YOLOv1 to v8: Unveiling Each Variant–A Comprehensive Review of YOLO," IEEE Access, vol. 12, pp. 42816-42833, 2024.
S. Hossain, H. Anzum, and S. Akhter, "Comparison of YOLO (V3, V5) and MobileNet-SSD (V1, V2) for Person Identification Using Ear-Biometrics," International Journal of Computing and Digital Systems, vol. 15, no. 1, pp. 1259-1271, 2024.
E. Casas, L. Ramos, E. Bendek, and F. Rivas-Echeverria, "YOLOv5 vs. YOLOv8: Performance Benchmarking in Wildfire and Smoke Detection Scenarios," Journal of Image and Graphics, vol. 12, no. 2, 2024.
N. Tanaka, H. Tanaka, M. Ikeda, and L. Barolli, "A Comparative Study of Four YOLO-Based Models for Distracted Driving Detection," in International Conference on Emerging Internet, Data & Web Technologies, Cham: Springer Nature Switzerland, pp. 362-370, Feb. 2024.
M. Alruwaili, M.H. Siddiqi, M.N. Atta, and M. Arif, "Deep learning and ubiquitous systems for disabled people detection using YOLO models," Computers in Human Behavior, vol. 154, p. 108150, 2024.
N.F. Alhussainan, B. Ben Youssef, and M.M. Ben Ismail, "A Deep Learning Approach for Brain Tumor Firmness Detection Based on Five Different YOLO Versions: YOLOv3–YOLOv7," Computation, vol. 12, no. 3, p. 44, 2024.
Ahmed, A.S. Imran, A. Manaf, Z. Kastrati, and S.M. Daudpota, "Enhancing wrist abnormality detection with YOLO: Analysis of state-of-the-art single-stage detection models," Biomedical Signal Processing and Control, vol. 93, p. 106144, 2024.
P.P. Khaire, R.D. Shelke, D. Hiran, and M. Patil, "Comparative Study of a Computer Vision Technique for Locating Instances of Objects in Images Using YOLO Versions: A Review," in International Conference on Information and Communication Technology for Intelligent Systems, Singapore: Springer Nature Singapore, pp. 349-359, Apr. 2023.
H. Deshpande, A. Singh, and H. Herunde, "Comparative analysis on YOLO object detection with OpenCV," International Journal of Research in Industrial Engineering, vol. 9, no. 1, pp. 46-64, 2020.
V. Goyal, R. Singh, A. Kumar, M. Dhawley, and S. Sharma, "Aerial Object Detection Using Different Models of YOLO Architecture: A Comparative Study," in Proceedings of International Conference on Computational Intelligence: ICCI 2021, Singapore: Springer Nature Singapore, pp. 333-345, Oct. 2022.
W. Xinming and T.S. Hong, "Comparative study on Leaf disease identification using Yolo v4 and Yolo v7 algorithm," AgBioForum, vol. 25, no. 1, 2023.
T. Diwan, G. Anirudh, and J.V. Tembhurne, "Object detection using YOLO: Challenges, architectural successors, datasets and applications," Multimedia Tools and Applications, vol. 82, no. 6, pp. 9243-9275, 2023.
T. Upadhyay, M. Aamash, M. Mittal, and G. Battineni, "Pest detection using state-of-the-art YOLO models: a comparative study," 2023.
K. Liu, H. Tang, S. He, Q. Yu, Y. Xiong, and N. Wang, "Performance validation of YOLO variants for object detection," in Proceedings of the 2021 International Conference on Bioinformatics and Intelligent Computing, pp. 239-243, Jan. 2021.
J. Doherty, B. Gardiner, E. Kerr, N. Siddique, and S.S. Manvi, "Comparative study of activation functions and their impact on the YOLOv5 object detection model," in International Conference on Pattern Recognition and Artificial Intelligence, Cham: Springer International Publishing, pp. 40-52, May 2022.
X. Wang, H. Li, X. Yue, and L. Meng, "A comprehensive survey on object detection YOLO," Proceedings, p. 0073, 2023. [Online]. Available: http://ceur-ws.org/Vol-1613/
M. Nawae, P. Maneelert, C. Choksuchat, T. Phairatana, and J. Jaruenpunyasak, "A Comparative Study of YOLO Models for Sperm and Impurity Detection Based on Proposed Augmentation in Small Dataset," in 2023 15th International Conference on Information Technology and Electrical Engineering (ICITEE), pp. 305-310, Oct. 2023.
Y. Zhou, "A yolo-nl object detector for real-time detection," Expert Systems with Applications, vol. 238, p. 122256, 2024.
