Research on pavement crack detection based on deep convolution and multi-scale feature fusion

Abstract

Abstract Efficient and accurate identification of highway pavement cracks is crucial for more precise road condition assessment. However, the performance of existing segmentation networks on pavement crack segmentation is often unsatisfactory due to challenges such as interruptions in continuous cracks and misidentifications caused by background noise. To address these issues, this paper proposes a deep learning network model (DCEM) based on multi-scale feature extraction and Dwconv-Edge Encoder feature fusion. Firstly, the Dwconv-Edge Encoder is utilized as the primary branch to extract edge features and contextual information of pavement cracks. Secondly, a dual-channel attention mechanism is integrated with a deep convolutional network, enabling the network to simultaneously focus on different dimensions of crack features. This approach effectively separates target regions, resulting in sharper pavement crack boundaries. Finally, the outputs of the three branches are fused to obtain a prediction map that intuitively determines the recognition results. Experimental results on a dedicated road pavement crack dataset demonstrate that the proposed DCEM model achieves superior performance compared to mainstream models such as HRNet, DeepLabv3+, PSPNet and FCN. Specifically, the DCEM model achieves the highest MIoU score (87.72%) and F1 score (91.82%).