SPCF-YOLO: Dense Small Target Detection Algorithm for Autonomous Driving

Abstract In the automatic driving scene, the pedestrian and vehicle targets are dense and block each other, and the problem of target false detection and missing detection is easy to occur. In this paper, a dense small target detection algorithm SPCF-YOLO is proposed. A new SPCSA module has been designed to strengthen the capture of edge features in occluded small targets. MPDIoU replaces the original CIOU loss function to address cases where the predicted and real frames have the same aspect ratio, but the aspect ratio difference of anchor frame is large. Experimental results show that in KITTI 2D data set, compared with the original YOLOv7 algorithm, [email protected] is 93.5%, an increase of 0.5%. [email protected]:0.95 is 66.9%, an increase of 2%; micro-target [email protected]:0.95 is 50.3%, an increase of 2.6%; and the computational load and parameter number remain basically unchanged, which proves the effectiveness of the design algorithm.