A novel framework for crack segmentation using image augmentation and a CannyNet

Computer-vision based crack detection is highly dependent on the quality of the segmentation process and remains a challenging task due to its complexity. In this paper, a framework for image segmentation that incorporates image augmentation method and a CannyNet is proposed to improve segmentation results. The style transfer is employed for image augmentation. A novel deep neural network for crack image segmentation, named CannyNet, is proposed to enhance the recognition capability for tiny cracks. Moreover, to improve the precision of CannyNet predictions, Bayesian optimization approach is employed to optimize network hyperparameters. The proposed framework for crack segmentation was verified using four open-source dataset and a new constructed dataset by conducting experimental test. A comparison of segmentation models indicates that style transfer method enhances the model's generalization, and the CannyNet demonstrates superior performance. The Bayesian optimization strategy is capable of optimizing the architecture of the CannyNet, thereby improving crack segmentation results.