Timely and accurate detection of road potholes in unstructured environments is crucial for ensuring traffic safety. Current detection algorithms face challenges related to missed detections and insufficient accuracy, particularly in complex scenarios. To enhance detection performance, an improved approach based on the YOLOv7 model is proposed. This method incorporates several enhancements: first, an enhanced hierarchical multi-scale fusion module is introduced to optimize feature extraction capabilities; second, the integration of an efficient channel attention mechanism enhances the model′s focus on critical target regions; finally, depthwise separable convolutions are employed to reduce computational complexity while maintaining high detection efficiency. The improved model was validated on the self-made dataset, compared with the existing YOLOv7x, YOLOv7-d6, YOLOv5x and YOLOv5m models, and the improved model was transferred to the public dataset. The evaluation metrics used include precision, recall (R), mean average precision, parameter count, and frames per second. The experimental results show that the improved model improves the precision, recall and average accuracy by 5.47%, 4.42% and 6.65%, respectively, and maintains a high efficiency in the detection speed. Compared with commonly used object detection models, the performance is excellent; after the transfer learning of public datasets, the precision, recall, and average accuracy are worth further improving. This improvement significantly enhances the detection performance and robustness of the model, not only strengthening the ability to ensure traffic safety, but also providing reliable technical support for autonomous driving.