Pedestrian　detection　in　a　crowded　environment　is　challenging　for　vehicle　intelligent　driving　systems.　At　present,　pedestrian　detection　algorithms　have　achieved　great　performance　in　detecting　well-separated　figures.　However,　pedestrians　are　generally　overlapped　in　crowded　scenes,　resulting　in　slow　detection　speed,　low　detection　accuracy,　and　poor　robustness　in　pedestrian　detection　technology.　In　this　paper,　the　YOLOv5　algorithm　is　used　for　pedestrian　detection.　In　the　aspect　of　data　pretreatment,　Mosaic　data　enhancement,　unified　image　size,　adaptive　anchor　frame　calculation,　and　other　processing　are　carried　out　for　data.YOLOv5　can　detect　targets　at　multiple　scales,　and　CIOU_Loss　and　DIOU_nms　are　applied　to　the　YOLOv5　algorithm.　It　can　improve　the　recognition　ability　of　the　occlusion　target　and　has　a　good　detection　effect　on　the　detection　of　the　occlusion　pedestrian　target　through　the　training　network　of　amplified　data　set.　The　verification　experiment　shows　that　the　pedestrian　detection　model　based　on　YOLOv5　has　great　detection　accuracy　and　recall　rate　in　detecting　covered　pedestrians.　©　2022　IEEE.