Multi-scale context feature and cross-attention network-enabled system and software-based for pavement crack detection

Abstract

Pavement crack detection continues to be a stubborn problem given the interference of various factors in the actual pavement and the complex topological structure of asphalt pavement. Among all the obstacles, the bottleneck of pavement crack detection lies in the difficulty of segmenting the cracks in the pavement images whose edges are blurred. This paper proposes a multi-scale context feature and cross-attention based on convolutional neural network for accurate and robust pavement crack segmentation. The multi-scale context feature module is built in different deep networks to extract rich crack feature information. Subsequently, in order to effectively promote the seamless integration of features at different levels, we deploy cross-attention modules to each branch. After that, we add deep supervision to each branch to accelerate training. Finally, we integrate the outputs of each branch to obtain the final output diagram. The comparative experiments on various pavement datasets show that the method has better robustness. At the same time, this paper designs a complete system of pavement crack detection (PCD) and develops corresponding engineering application software. The PCD system can record the real-time pavement image data to the edge server, and the client can also monitor the real-time pavement images from the edge server through the HTTP protocol.