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Towards Real-Time Crack Detection Using a Deep Neural Network With a Bayesian Fusion Algorithm

Surface cracks can represent very small and thin objects in images. With irregular shapes and sizes, and non-fixed texture patterns, the detection of cracks can be a challenging problem in computer vision. Prior work has been undertaken on detecting cracks for images using a sliding window mode. However, such methods can be time consuming, and result in high false alarms. To help address this problem, a new crack detection and segmentation method is proposed in this paper. Specifically, our method includes three main features: (1) a Faster R-CNN model to detect crack patches in images; (2) the use of a Bayesian fusion algorithm to suppress false alarms based on detected patch orientation; and (3) image processing functions to obtain final segmentation masks, such as for Gaussian blur, erosion, etc. Experimental results show that our method can achieve high detection accuracy on sampled images in real-time.