Joint Optimization of Crack Segmentation With an Adaptive Dynamic Threshold Module

Crack segmentation is a critical component in structural health monitoring. Conventional crack segmentation models usually focus on optimizing the cross-entropy-based objective function and overlook the optimization of the subsequent binarization process. In this paper, we redefine the crack segmentation problem as a joint optimization problem, which requires optimizing the binarization process in addition to the objective function of the segmentation model. Simultaneously optimizing both processes demonstrates significant improvements in the model's segmentation performance. To optimize the binarization process, we propose the Adaptive Dynamic Thresholding Module (ADTM), which reuses the spatial features in the segmentation network to perform an additional regression task to obtain the optimal threshold for each crack image. ADTM is a pluggable component for practical deployment, consuming only a small amount of additional memory during deployment while significantly improving inference accuracy. Experimental results using four different datasets with diverse sources and distributions for crack semantic and instance segmentation demonstrate the effectiveness of ADTM in improving segmentation performance. While ADTM has only been evaluated on cracked data, our findings suggest its potential to improve the performance of other binary classification image segmentation problems.