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%.