A multi-scale learning method with dilated convolutional network for concrete surface cracks detection

Abstract

Concrete surface cracks detection is an important task to ensure the safety of infrastructure. Because of the complexity of background and low contrast of concrete surface, it is difficult to detect the cracks on the concrete surface accurately. To tackle this problem, a multi-scale dilated convolutional method for concrete surface crack detection is proposed to improve the accuracy of detection. The proposed network is based on the encoder-decoder structure of U-Net. Cascade multi-scale dilated convolutions in the centre of the network is used to get larger receptive field without additional parameters. In the decoder stage, the feature fusion module is used to integrated the multi-scale and multi-level side network feature for the final prediction. A large crack dataset is collected as a training set and other three smaller datasets are used for evaluation. Extensive experiments have been conducted on these three crack datasets, which achieves optimal dataset scale (ODS) F-score over 0.84, optimal image scale (OIS) F-score over 0.85 and average precision (AP) over 0.86. This algorithm performs better than the current crack detection, edge detection and semantic segmentation methods.