裂缝是最常见的路面病害之一，会影响到道路行车安全。论文针对人工排查和测定路面裂缝成本高、耗时长等问题，提出了一种基于视觉传感智能检测路面裂缝最大缝宽的方法。该方法优化了DAUNet网络，融合了注意力机制，提高了裂缝图像分割的精度；然后将分割出来的裂缝通过腐蚀迭代、连通域判别及象限划分等处理，可以更加准确计算出不同走向裂缝的最大缝宽。实验结果表明：论文中优化后的DAUNet在评价指标sOIS方面提高了3.15%，计算最大缝宽的精度相比于目前较优的最大缝宽计算方法提高了3.09%，时间缩短了89.06%。

Cracks are one of the most common pavement diseases, which will affect road traffic safety. To address the high cost and time-consuming challenges associated with manual investigation and determination of pavement cracks, a method based on image processing is proposed to intelligently detect the maximum crack width. In this paper, the DAUNet framework is optimized, the attention mechanism is integrated, and the accuracy of crack segmentation is improved. Then, the segmented cracks are processed through corrosion iteration, connected domain discrimination, and quadrant division, so that the maximum width of cracks with different directions can be calculated more accurately. Experimental results show that the optimized DAUNet has improved the evaluation index sOIS by 3.15%, the accuracy of calculating the maximum crack width has increased by 3.09% in comparison with the current optimal maximum crack width calculation method, and the time has been shortened by 89.06%.