LAUSR

Crack detection for concrete pavement is an important and fundamental task to ensure road safety. However, automatic crack detection is a challenging topic due to the complicated concrete pavement background and the diversity of cracks. Inspired by the latest developments of deep learning in computer vision, we propose a novel crack detection algorithm of concrete pavement based on attention mechanism and multi-features fusion, and make it possible to deal with various cracks in different pavement backgrounds. The proposed network is constructed using the encoder-decoder structure. The architecture of the encoder part is consisted of Res2Net modules with attention mechanism to achieve fast focus of cracks. Cascade and parallel mode dilated convolutions are set as the center part to enlarge the receptive field of feature points without reducing the resolution of the feature maps. The decoder integrates multiple side output feature maps for pavement crack detection in the manner of feature pyramid. We use ODS, OIS and AP to evaluate the performance of our network. To demonstrate the validity and accuracy of the proposed method, we compare it with some existing methods. The experimental results in multiple crack datasets reveal that our method is superior to these methods.