MFAFNet: An innovative crack intelligent segmentation method based on multi-layer feature association fusion network

Abstract

The regular and timely detection and segmentation of cracks in asphalt pavements is very important for road evaluation. However, certain problems remain to be solved, such as the difficulty of crack detection under interference from zebra stripes and dark light. A novel intelligent crack segmentation method based on a multi-layer feature association fusion network (MFAFNet) is therefore proposed in this study. Firstly, a fusion convolution with a Transformer called a feature coupling encoder (FC-Encoder) is developed in MFAFNet to enhance the model's global feature awareness and ability to capture local details. Secondly, a two-branch feature association module (TFBA-module) is proposed to obtain strong correlation information for global and local features. Next, a hybrid MLP architecture for lightweight feature decoder (H-MLP Decoder) is constructed to achieve feature recombination of the multi-level fusion information. The accuracy of crack segmentation under interference from zebra stripes and dark light is improved through the use of these three modules. Our model is compared with some well-known existing segmentation networks such as U-Net, DeepLabv3+, PSPNet, HRNet and SegFormer, and the segmentation accuracy, F1-score and IoU of the proposed MFAFNet are found to be 94.39 %, 93.26 % and 89.46 %, respectively. In addition, the segmentation efficiency reaches 21.87FPS. Our experimental results show that the proposed MFAFNet yields more effective performance in the field of asphalt pavement crack segmentation than the alternatives.