Cracks are one of the main road surface diseases, and timely and effective crack detection and evaluation are crucial for road maintenance. To achieve fast and accurate semantic segmentation of road crack images, a road crack detection method based on the DeepLabv3+ model is proposed. To reduce the number of model parameters and improve inference speed, MobileNetv3 is used as the model's backbone feature extraction network, and Ghost convolution is used instead of ordinary convolution in the atrous spatial pyramid pooling module to make the model lightweight. To avoid degrading model accuracy by replacing the backbone network, the following measures are adopted. First, a strip pooling module is used in the atrous spatial pyramid pooling module to effectively capture the contextual information of crack structures while avoiding interference from irrelevant regional noise. Second, a lightweight channel attention mechanism, the effective channel attention (ECA) module, is introduced to enhance the feature expression ability, and a shallow feature fusion structure is designed to enrich the image's detailed information, optimizing the model's crack recognition effect. Finally, a mixed loss function is proposed to address the issue of low detection accuracy caused by imbalanced categories in the crack dataset, and transfer learning training is used to improve the model's generalization ability. The experimental results show that the proposed road crack detection model's parameters are only 14.53 MB, which is 93.04% less than the original model parameters, and the average frame rate reaches 47.18, meeting the requirements of real-time detection. In terms of accuracy, the intersection to union ratio and F1 value of this model's crack detection results are 57.21% and 72.76%, respectively, which are superior to classic DeepLabv3+, PSPNet, and U-Net models, as well as advanced FPBHN, ACNet, and other models. The proposed method can significantly reduce the number of model parameters while maintaining road crack detection accuracy and meeting real-time requirements, thus laying the foundation for online detection of road cracks based on semantic segmentation.