An improved YOLOv4 algorithm for pedestrian detection in complex visual scenes - Shuai KANG; Jianwu ZHANG; Zunjie ZHU; Guofeng TONG (Dianxin kexue)
At present, the difficulty of pedestrian detection has been dramatically increased because of some problems, such as the dark or exposed illumination, bad weather, serious occlusion, large difference size of pedestrians and blurred images in complex visual scenes.Therefore, an improved YOLOv4 algorithm was proposed, which improved the detection performance of pedestrian detection in complex visual scenes, aiming at the problems of low accuracy and highly missed detection rate.Firstly, the self-annotation data set pedetrian were constructed.Secondly, the hybrid dilated convolution (HDC) was added into the backbone network to improve the ability of pedestrian feature extraction.Finally, in order to obtain more detailed feature, the spatial jagged dilated convolution (SJDC) structure was proposed to replace the spatial pyramid pooling structure.The experimental results show that the average precision (AP) of the proposed algorithm can achieve 90.08%.The proposed algorithm can substantially improve AP by 7.2%, and the log-average miss rate (LAMR) reduce by 13.69% compared with the original YOLOv4 algorithm.