MAN K, SUN D X, LIU X L,et al.Lightweight crack segmentation method for underground spaces integrating a multi-scale attention mechanism[J].Journal of Henan Polytechnic University(Natural Science) ,2026,45(4):105-113.

doi:10.16186/j.cnki.1673-9787.2025100033

Received:2025/10/24

Revised:2026/03/23

Published:2026/06/17

Lightweight crack segmentation method for underground spaces integrating a multi-scale attention mechanism

Man Ke1, Sun Dongxing1, Liu Xiaoli2, Song Zhifei1, Deng Xifei3, Ju Xingjun4

1.School of Civil Engineering， North China University of Technology， Beijing  100144， China;2.State Key Laboratory of Hydroscience and Engineering， Tsinghua University， Beijing  100084， China;3.China Railway No.4 Engineering Group Co.， Ltd.， Hefei  230023， Anhui， China;4.Baorixile Energy Co.， CHN Energy， Hulunbuir  021008， Inner Mongolia， China

Abstract: Objectives With the rapid development of urban rail transit systems in China， the safety inspection demands for underground space structures have increased significantly. Existing deep learning-based crack detection models suffer from large parameter sizes and high computational complexity， making it difficult to satisfy the requirements of real-time on-site detection. Therefore， a high-precision lightweight detection method suitable for real-time deployment on mobile terminals and edge devices was developed. Methods An improved DeepLabV3+ model was proposed for structural crack detection. The improved model integrates a hybrid receptive field design and employs dilated convolutions with progressive dilation rates to effectively enlarge the receptive field while avoiding grid artifacts. A lightweight MobileNetV2 was adopted to replace the original backbone network， thereby reducing feature channel dimensions. GhostNet convolution operations were introduced into the decoder to generate redundant feature maps through linear transformations， reducing the number of parameters and computational cost. In addition， an enhanced crack attention module based on directional feature extraction was designed by combining multi-scale convolution fusion and channel attention mechanisms， thereby improving the perception of crack edges and complex propagation patterns. The loss function was optimized by integrating weighted Dice loss and Focal loss to improve sensitivity to fine cracks and alleviate class imbalance. Furthermore， high-level and low-level semantic features were fused to enhance crack representation capability. Results The results show that the number of model parameters is reduced to 25% of the original model， while the F₁-score， mean intersection over union， and mean pixel accuracy on the test set are increased by 3.37%， 5.53%， and 6.2%， respectively.  Conclusions The improved model effectively reduces feature information loss and enhances the recognition capability for crack edge details and complex propagation patterns， demonstrating good generalization performance. The proposed algorithm provides a new approach for the automated monitoring of structural defects in tunnels， deep foundation pits， and other underground spaces.

Key words:deep learning;crack detection;semantic segmentation;attention mechanism;dilated convolution