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Road potholes are the main road defects that can damage vehicles, affect driver safety, and even lead to traffic accidents in severe cases. To address this issue, this paper proposes an improved YOLOv7 road pothole detection algorithm. Firstly, use Mosaic+Mixup for built-in data augmentation, expand small sample datasets, and enhance model generalization ability; Secondly, the CA attention mechanism is introduced to encode the horizontal and vertical position information, ensuring both computational complexity and attention to a wide range of position information; Then, the BIFPN bidirectional feature pyramid network is introduced to improve detection efficiency through feature fusion of multi-scale semantic features; Finally, CIoU is replaced by the loss function SIoU, which effectively solves the sample imbalance problem in regression. The experimental results show that the improved algorithm achieves a mean and accuracy of 89.42% and 90.12% in flat and uneven datasets, which is 6.18% and 1.96% higher than the original YOLOv7 version. It is more accurate and fast in road maintenance applications.