Application of Lightweight Target Detection Algorithm Based on YOLOv8 for Police Intelligent Moving Targets

This study presents an intelligent moving target to replicate mob attacks and other realistic events in police training to match actual fighting needs. The police intelligent moving target must deploy target detection algorithms on the hardware platform, but the traditional you only look once (YOLO)v8 algorithm has a large framework, which will slow recognition due to the hardware platform’s lack of arithmetic power. In this study, GhostNet network architecture replaces YOLOv8^′s backbone network for real-time target identification, improving recognition speed. The bounding box regression issue in target detection uses the scale invariant intersection over union (SIoU) loss function to increase prediction box overlapping and identification accuracy. Finally, BiFormer uses dynamic sparse attention for more flexible computational allocation and content perception. The method’s real-time detection speed is 4.81 frames per second (FPS) faster, [email protected] is 5.38% faster, mean average precision (mAP)@0.5:0.95 is 4.19% faster, and parameter volume is 5.81 M less than the original approach. The approach developed in this work has several applications in real-time target identification and lightweight deployment.