Segmentation refinement of thin cracks with Minimum Strip Cuts

Accurate segmentation is crucial for dataset labeling and morphological analysis of thin cracks, for which small segmentation error may take great effect to the results. However, due to the complex morphology and thin structures, so far it is still very hard to accurately segment thin cracks either by manual or by segmentation algorithms. In this paper we propose an approach for accurate and efficient segmentation of thin cracks. The core of our approach is an optimization-based image segmentation method designed specifically for extracting optimal thin strip regions from images. Our method can be considered as an extension of previous minimum path search problem, so we call it as Minimum Strip Cuts, or StripCuts for short. An effective objective function for segmentation refinement of thin cracks is proposed, whose global optimal solution can be obtained efficiently based on the proposed volumetric dynamic programming and crack linearization methods. Our method is robust to low-contrast cracks and complex background, and can run in real-time. Therefore, it can be used for refining exist datasets, and also for the post-processing of crack segmentation methods. Based on the proposed refinement method, we introduce a new crack segmentation dataset RefinedCracks, which provides accurate refined annotations for previous main crack segmentation datasets. The importance of refinement to the training and evaluation of crack segmentation methods is also verified by both quantitative and qualitative evaluations.