MFDB-Net: Multi-Attention Fusion Dual-Branch Network for Pavement Crack Detection

Cracks are the most common disease in road maintenance work. They will accelerate the aging and damage of the pavement and bring potential risks to driving safety. Automatic detection of pavement cracks ensures infrastructure road safety. However, automatic detection of pavement cracks has always faced many challenges, such as irregular crack shapes and background noise interference. The existing methods ignore the correlation between cracked pixels, and there are some problems such as loss of detail information, lack of feature fusion and semantic gap. A Multi-Attention Fusion Dual-Branch Network(MFDB-Net) is proposed to accurately detect complete cracks. In this network, a dual-branch network is used as the backbone. A deeper branch extracts the semantic information of cracks, and a shallower branch retains more crack detail information. In addition, multiscale features are extracted by connecting multiple dilated convolutions in series with different convolution kernel sizes, which establishes global context connections. The attention mechanism is introduced to extract high-level network features and low-level network features respectively. Fusing features brings into play the characteristics of different network layers. Five publicly available crack datasets are used in our experiments, and the assessment is performed based on six evaluation metrics. We achieve F1-score of 0.879 and MIoU of 0.886 on the DeepCrack, F1-score of 0.737 and MIoU of 0.775 on the Crack500, F1-score of 0.571 and MIoU of 0.698 on the CrackTree200, and F1-score of 0.602 and MIoU of 0.712 on the EdmCrack600. We also carry out the generalization experiments on CFD. Sufficient experiments demonstrate our method present superior performance compared to other methods.