Lightweight Crack Image Segmentation Method Based on Dilated Convolution
XIE Yonghua
BAI Yong
LI Tao
ZHUO Annan
Abstract：The traditional crack detection methods are mainly manual and static detection,and the detection results are sub-jective and the data processing speed is slow.Aiming at the problems existing in the traditional detection method,a semantic seg-mentation based method is proposed to rapidly realize end-to-end crack segmentation.The residual structure is introduced to pre-vent network degradation caused by deepening network layers of UNet.On this basis,dilated convolution is used to solve the prob-lem that the feature map size is too small in the late stage of feature extraction,which leads to insufficient feature extraction.Based on the detailed information of the crack edge features,convolution block attention module(CBAM)is added to optimize the weight distribution of features and suppress irrelevant features in the target region.The results show that compared with several common seg-mentation models,the number of parameters is reduced by 11.77%on average,and the test time of CrackTree260 is reduced by 20.92%,which can be well applied to the actual engineering detection scenario.
Keywords：UNetdilated convolutionresidualcrack segmentationattention mechanism
Publication Date：2025-12-20
Online Publishing Date：2026-03-09(First online date of this platform, not the publication date of the document)
Pages：7( 3545-3550,3556 )