Objective To address the issues of large model parameters poor real-time detection performance and low detection accuracy due to complex road environments in existing road surface pothole detection models an improved YOLOv8-based road surface pothole detection algorithm is proposed. Methods First the neck network was replaced with a bi-directional feature pyramid network BiFPN to achieve multi-scale feature fusion. Second to enhance the inference speed of the model a diverse branch block DBB was used to replace the convolutional layers in the C2f module and a reparameterization module C2f-DBB was introduced to substitute part of the C2f modules. Then a channel prior convolutional attention CPCA mechanism was introduced at the end of the backbone network to retain channel priors while effectively extracting spatial relationships thereby strengthening feature extraction capabilities. Finally the boundary box regression loss function MPD intersection over union MPDIoU was used instead of the original model?? s complete intersection over union CIoU further improving the accuracy of the algorithm. Results Experimental results showed that the improved algorithm increased the average precision by 3. 1% compared to the original network with the model parameters being only 5. 9 M and the computation being 7. 3 G representing reductions of 6. 3% and 9. 8% respectively. The frame rate reached 53. 4 frames per second. Conclusion The experimental results indicate that the improved model significantly enhances accuracy compared to current mainstream models while meeting real-time requirements providing valuable reference for practical applications.