基于改进YOLOv8的道路障碍物检测模型OA

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 perform-ance 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 ima-ges.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 demon-strate that,compared to the baseline model YOLOv8,the improved YOLOv8-J model achieves increa-ses 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.