TriCrackNet: Trilateral Segmentation Network for Real‐Time Crack Segmentation

ABSTRACT To achieve high‐precision real‐time crack segmentation, we propose TriCrackNet, an efficient network based on a tri‐branch collaborative architecture incorporating boundary constraints, semantic parsing, and spatial refinement. In the semantic branch, efficient atrous spatial pyramid pooling (EASPP) is integrated. The EASPP performs feature grouping and efficiently utilises each group's features through hierarchical residual connections. Simultaneously, it employs progressive dilated convolutions with incrementally expanding receptive fields to comprehensively capture multi‐scale crack features. Meanwhile, an efficient feature enhancement module (EFEM) was inserted between the semantic branch and the other two branches. This module selectively utilises global features from the semantic branch to provide semantic guidance for the other branches, while enabling self‐adaptive enhancement of local features in the target branches based on their own feature characteristics. Finally, the features of the last three branches are efficiently fused through an efficient feature fusion module that performs adaptive spatial weight weighting. Compared with other state‐of‐the‐art methods, TriCrackNet achieves the best trade‐off between inference speed and accuracy on CrackForest, Crack500, and DeepCrack datasets.