Chinese Journal of Liquid Crystals and Displays, Volume. 38, Issue 5, 666(2023)
Rail surface crack detection algorithm based on improved YOLOv5s
Fig. 1. Sample images of three different levels of sleeper surface cracks.(a)sneg;(b)mneg;(c)lneg.
Fig. 8. Feature pyramid network(FPN)and path aggregation network(PAN)
Fig. 12. Two cross scale connection optimizations implemented by BiFPN
Fig. 15. Comparison of detection results before and after improvement.(a)Original drawing;(b)YOLOv5s detection effect drawing;(c)YOLOv5s-CBE detection effect drawing.
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Miao-sen ZHOU, Quan-wu TANG, Tian-tian SHI, Tong-lan LUO, Ze-xin ZHANG, Yong-xia XUE. Rail surface crack detection algorithm based on improved YOLOv5s[J]. Chinese Journal of Liquid Crystals and Displays, 2023, 38(5): 666
Category: Research Articles
Received: Aug. 13, 2022
Accepted: --
Published Online: Jul. 4, 2023
The Author Email: Quan-wu TANG (tangqw@nxu.edu.cn)