Pavement crack segmentation algorithm based on improved SegNet
Pavement cracks are common potential hazards threatening the safety on highways.Classical pavement crack detection algorithms suffer such problems as different degrees of crack breaks and poor recognition of the thin and fine edges of cracks.This paper proposes a Crack SegFormer model based on an improved SegNet network,mainly comprising three parts:an encoder based on crack localization attention,a multilayer feature pyramid,and a decoder based on crack sharpening attention.The effectiveness of the Crack SegFormer model in segmenting cracks is verified based on three publicly available data from Crack500,Crack200,DeepCrack and CFD.Our results show the proposed Crack SegFormer model is able to suppress non-cracking features and retain fine and end-cracking features.Compared with the classical SegNet network,it improves the accuracy by 1.14%,the recall rate by 3.61%and F1-score by 4.26%.
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