Research on Small Object Detection Method Based on Artificial Intelligence and Deep Learning
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.