Abstract:

At present, the deep learning instance segmentation method for crack detection mainly generates a boundary box through target detection to segment pixel by pixel mask, which will affect the detection effect of structural crack contour, and is accompanied by complex post-processing cost. To solve this problem, this study proposes to use deep snake algorithm model of deep deformable contour to identify and detect structural cracks. The robustness of the model is improved by data enhancement of the structural crack data set. At the same time, the pre training network parameters on the large image data set coco are transferred to the structural crack segmentation model as initialization by transfer learning. The experimental results on the self-made crack image data set show that the trained model can correctly identify the crack object and complete the segmentation of multiple crack targets at the same time. On the premise of the average detection time of 0.12 s, the AP50 reaches 75.4%. The comparison between the proposed methool and other deep learning models and edge detection algorithms also reflect the advantages of Deep Snake algorithm.

Key words: deep learning, instance segmentation, object detector, Deep Snake, structural cracks, data augmentation, transfer learning, crack image data set