摘要
道路障碍物检测是自动驾驶环境感知的重要内容。针对当前道路障碍物检测算法精度有待提升等问题,提出改进YOLOv5s的道路障碍物检测算法。首先引入改进坐标注意力模块,过滤多尺度特征图的无效信息,强化关注感兴趣区域。其次使用增强降采样模块缓解融合网络下采样过程的重要信息丢失,增强特征鲁棒性。最后优化算法回归损失,明智的梯度增益分配策略,提升了普通质量锚框损失贡献度。试验结果显示,改进模型在数据集上的平均精度均值较原YOLOv5s提高了4.2%,达到了78.6%,同时也优于Faster R-CNN、YOLOX、YOLOv7等算法。所提算法具有42帧/s的检测速度,满足实时检测的要求。本研究提出的改进算法能够有效提高道路障碍物检测精度,具有实际应用潜力。
Road obstacle detection is a crucial component of automatic driving environment perception.To enhance the precision of existing road obstacle detection algorithms,we propose an improved YOLOv5s road obstacle detection algorithm.The improved coordinate attention module filters invalid information from multi-scale feature maps and strengthens the focus on areas of interest.Additionally,the enhanced downsampling module alleviates the loss of essential information during sampling in the fusion network,thereby increasing feature robustness.The optimized algorithm's regression loss and wise gradient gain allocation strategy improve the contribution of common mass anchor frame loss.Experimental results demonstrate that the improved model's average accuracy on the dataset has increased by 4.2%to 78.6%,outperforming Fast R-CNN,YOLOX,YOLOv7,and other algorithms.With a detection speed of 42 frames per second,the algorithm meets real-time detection requirements.Therefore,the proposed improved algorithm in this study can effectively improve the accuracy of road obstacle detection and has practical application potential.
作者
冉险生
李锐
贺帅
Ran Xiansheng;Li Rui;He Shuai(Shool of Mechatronics and Vehicle Engineering,Chongqing Jiaotong University,Chongqing 400074,China)
出处
《电子测量技术》
北大核心
2023年第22期177-185,共9页
Electronic Measurement Technology
