Multi-Scale Deep Pixel Distribution Learning for Concrete Crack Detection

A number of methods including image processing technologies (IPTs) and deep learning methods, have been used to detect defects in civilian infrastructure. These methods have been introduced to extract features representing cracks in concrete surfaces. Inspired by recent advances of a pixel distribution learning method in background subtraction, we propose a novel multi-scale deep learning method (MS-DPDL) for concrete crack detection. The designed CNN network is trained on the dataset CRACK500 [1], [2] and tested on it for concrete segmentation. To show good transferability of our proposed model, it is later tested on the dataset Concrete Crack Images for Classification [3]. Several existing deep learning methods are used to compare the performance of the proposed MS-DPDL method. Results show that our method has good performance and can effectively find concrete cracks in practical situations.