A semantic segmentation method based on an improved DeepLabv3+network is proposed to address the issues of low detection accuracy and large errors associated with traditional semantic segmentation techniques for detecting asphalt road cracks. In the encoder stage ,this method replaces the backbone network Xception of DeepLabv3+ with lightweight MobileNetv2 ,thereby reducing the numberofparameters. In the decoderstage ,a dualattention mechanism is incorporated to further improve the segmentation accuracy of the network. The Dice loss function is combined with the original cross-entropy loss function to alleviate the imbalance between foreground and background in the sample. Extensive experiments were conducted on real-time road detection data. The results indicate that ,compared with the original DeepLabv3+ ,the averageintersection-to-union ratio (mIoU) and average pixelaccuracy (mPA) achieved bythe proposed method werehigherby8. 98% and 17. 39% ,respectively. As compared with other mainstream semantic segmentation models ,the improved DeepLabv3+ also exhibits good performance in detecting asphaltroad cracks.