Abstract: Road obstacle detection is a significant part of intelligent driving technology. In response to the current problems of low accuracy in detecting small obstacles on roads, poor detection performance in adverse environmental scenes, and scarcity of road obstacle datasets, a suitable obstacle dataset for road scenes is organized and constructed. Based on the YOLOv8 model, a new model, YOLOv8-J with high detection accuracy is proposed. Firstly, a lightweight backbone network called LskViT based on RepViT is designed to enhance the model’s ability to extract multi-scale features. Secondly, the SPD-Conv convolutional module is introduced to strengthen the model’s learning capability for low-resolution images. Finally, an additional small object detection layer is added to help the model capture more shallow features, thereby improving its detection performance for small obstacles. Experimental results demonstrate that, compared to the baseline model YOLOv8, the improved YOLOv8-J model achieves increases of 5.9 percentage points and 6.1 percentage points in mAP@0.5 and mAP@0.5:0.95 values, respectively. The improved model is well-suited for road obstacle detection tasks and further enhances detection performance for small obstacles in adverse environments.

Key words: road obstacle, attention mechanism, convolutional module, model optimization, YOLOv8 model