id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
fcis-3164	Zhang, Ziyi; Ding, Xuewen	Small pedestrian target detection based on YOLOv5	2022	3	.pdf	application/pdf	1771	108	54	Abstract: YOLOv5s is the network with the smallest depth and feature map width and the fastest image inference, but when applied to small pedestrian target detection in complex scenes, the detection still suffers from wrong and missed detections. As pedestrian detection in realistic environments is unavoidably affected by the environment, e.g. exposure and shadow surfaces due to strong daylight exposure; blurred pedestrian features caused by foggy[5] and rainy[6] weather; the distance of pedestrians from the camera in surveillance scenes, which can lead to differences in scale spanning; and the small pedestrian problem caused by the dense pedestrian flow in special scenes such as high-speed railway stations, airports, and public gathering places[7] can all affect the effectiveness of detection.	cache/fcis-3164.pdf	txt/fcis-3164.txt