A Method of Hierarchical Feature Fusion and Connected Attention Architecture for Pavement Crack Detection

Abstract

Automatically detecting cracks with uneven strength from a complex background is a valuable and challenging issue. In light of the lost details and the incomplete extracted cracks in the process of crack extraction, we propose a network model with hierarchical feature fusion and connected attention architecture. Firstly, we build the backbone network on the improved DCA-SE-ResNet-50. Then, we propose a method for crack feature fusion, which combines depthwise separable convolution and dilated convolution to recover more crack details. Finally, we design the attention layer which integrates feature map2 with feature map4. The side network incorporates the feature maps of the low convolutional layer and the high convolutional layer at multiple levels to assist in obtaining the final prediction map. Sufficient experimental results demonstrate that our method achieved state-of-the-art performances, best F-score over 0.86, 12 FPS. Besides the effectiveness of our proposed method is verified on CFD, Crack500, and DCD datasets.