To further improve the performance of small object detection in images,a small object detection method based on im-proved YOLOv5 is proposed.Among them,the YOLOv5 network is used as the basic object detection method,and the detection per-formance of the network is further improved by pruning its feature layer,introducing a feature pyramid network,and fast spatial pyra-mid pooling.The experimental results show that compared with the unmodified YOLOv5 network,the improved YOLOv5 network has better detection accuracy,good lightweight effect,and significantly improved object detection efficiency;Compared with other object detection algorithms,the small object detection method based on improved YOLOv5 can achieve good network lightweight effect while maintaining high detection accuracy,and has higher detection efficiency,mAP@0.5 It is 74.98.In summary,the designed detection method based on improved YOLOv5 has excellent performance and can perform effective small object detection.It can be applied to practical detection scenarios with high feasibility.
维普
万方数据