Exploring Object Detection Algorithms and implementation of YOLOv7 and YOLOv8 based model for weapon detection
Keywords:
anchor box, classifier, feature map, RCNN, YOLOAbstract
This paper explores the working principles, performance metrics, and architectural nuances of various object detection techniques, focusing mainly on the YOLO (You Only Look Once) algorithms. A comprehensive comparative study is conducted, considering factors such as loss function, backbone network, and performance on standardized image sizes. Beginning with an introduction, the paper classifies object detection algorithms, into one-stage and two-stage object detection techniques. The literature review scrutinizes the operational mechanisms and constraints of existing techniques. This study then transitions into weapon detection using the YOLOv7 and YOLOv8 algorithms, leveraging a dataset sourced and pre-processed from the Roboflow website. The mean Average Precision (mAP) achieved by YOLOv7 and YOLOv8 after training for 50 epochs stands at 0.9289 and 0.9430, respectively. Furthermore, the paper elucidates how performance metrics fluctuate with respect to epoch count in YOLOv7. In conclusion, the paper outlines avenues for further research, highlighting areas that warrant attention and exploration within the realm of object detection methodologies.
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