Abstract
In this paper, a method for the detection of multi-scale cracks based on computer vision is introduced. This crack detection method is divided into two parts to extract crack features from the images. In the first part, the original image is mapped to different scale Spaces and the pixels with strong ridge characteristic are detected with a Hessian-matrix in these scales Spaces. Then the detection results in different scales are superimposed. Finally, an evaluation index is designed to select the Keypoints detected in the previous step. In the second part, the cracks are detected based on a modified Fast Marching Mothed which is improved into an iterative algorithm with self-terminating capability. The Keypoints detected and selected in the first part are used as endpoints for the crack detection. Then the burrs are removed from the detection results. The experimental results show that under different lighting and road conditions, the crack feature can be extracted stably by this method.
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Acknowledgements
This work is supported by National Natural Science Foundation of China (Grant Nos.U1713207 and 52075180), Science and Technology Program of Guangzhou (Grant Nos.201904020020), and the Fundamental Research Funds for the Central Universities.
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Wang, N., Zhu, H., Zhang, X. (2020). Multi-scale Crack Detection Based on Keypoint Detection and Minimal Path Technique. In: Chan, C.S., et al. Intelligent Robotics and Applications. ICIRA 2020. Lecture Notes in Computer Science(), vol 12595. Springer, Cham. https://doi.org/10.1007/978-3-030-66645-3_36
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