Optimizing Real-Time Object Detection- A Comparison of YOLO Models

Pravek Sharma; Dr. Rajesh Tyagi; Dr. Priyanka Dubey

doi:10.55524/ijircst.2024.12.3.11

Abstract

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.

Keywords

Weapon Detection; YOLO models; Security; Deep Learning; Learning Rate

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Cites this article as

P. Sharma, D. R. Tyagi, D. P. Dubey, "Optimizing Real-Time Object Detection- A Comparison of YOLO Models", International Journal of Innovative Research in Computer Science and Technology (IJIRCST), Vol-12, no.3, pp.57-74, 2024. Available from: https://doi.org/10.55524/ijircst.2024.12.3.11

Corresponding Author

Pravek Sharma

Integrated M.Tech Student, Amity School of Engineering and Technolodgy, Amity University Gurugram, Haryana, Gurugram, India

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Optimizing Real-Time Object Detection- A Comparison of YOLO Models

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