Accurate segmentation of road cracks is vital for identifying cracks in pavements for greater traffic safety. However, owing to blurred boundaries, low contrast between cracks and the surrounding environments, changes in color and shape, most existing segmentation methods face significant challenges in obtaining receptive fields and extracting image feature information. To overcome these issues, we constructed a new framework, named ASARU-Net, to analyze and segment road cracks. We utilized a superimposed U-Net architecture instead of the original 3 × 3 convolution layer to improve the receptive field and enhance segmentation performance. Then, we employed a convolution block in the last layer of the decoding path to obtain more discriminative features. Moreover, the residual mechanism was integrated into a spatial squeeze-and-excitation layer and convolutional block attention module, which improved sensitivity and prediction accuracy. A mixed loss integrating binary cross-entropy and Jaccard loss was used to ensure more balanced segmentation. The proposed method was applied on CRACK500 image database, and achieved a superior performance with Dice, Jaccard, and accuracy values of 79.83%, 69.92%, and 96.94%, respectively. The quantitative and qualitative experimental results show that our method achieves high-performance road crack segmentation and can adapt to cracks with complex background and more interference.