Cracks represent a significant hazard to pavement integrity， making their efficient and automated extraction essential for effective road health monitoring and maintenance. In response to this challenge， we propose a crack automatic extraction network model that integrates multi‑scale image features， thereby enhancing the model’s capability to capture crack characteristics and adaptation to complex scenarios. This model is based on the ResUNet architecture， makes modification to the convolutional layer of the model， proposes to construct multiple branches utilizing different convolution kernel sizes， and adds a atrous spatial pyramid pooling module within the intermediate layers. In this paper， comparative experiments on the performance of the basic model， ablation experiments， comparative experiments before and after data augmentation， and generalization verification experiments are conducted. Comparative experimental results indicate that the improved model exhibits superior detail processing capability at crack edges. The overall performance of the model， as measured by the F1‑score， reaches 71.03%， reflecting a 2.1% improvement over the conventional ResUNet.
Biheng et al. (Mon,) studied this question.