id	author	title	date	pages	extension	mime	words	sentence	flesch	summary	cache	txt
fcis-21118	Mei, Chongbin; Wen, Yucheng	Subway Tunnel Crack Identification based on YOLOv5	2024	8	.pdf	application/pdf	4642	308	54	It is difficult for traditional methods to meet the current requirements for tunnel crack detection. In recent years, with the rapid development of big data and artificial intelligence, deep neural networks, as a discriminative structure algorithm, have excellent performance in image classification and target detection, and are suitable for scenarios with large amounts of data such as tunnel crack detection.	cache/fcis-21118.pdf	txt/fcis-21118.txt