YOLOv5的改进算法及其在自动驾驶多目标检测的应用研究

Research on Improved YOLOv5 Algorithm and Its Application in Multi-Object Detection for Automatic Driving

车辆、行人等交通参与者的实时检测是自动驾驶汽车与外界环境实现信息交互的重要环节,而在复杂天气条件下多目标检测精度低仍是一个挑战性问题。提出了一种YOLOv5的改进算法及其在自动驾驶多目标检测的应用方法。该方法采用K-means++算法对数据集中的目标样本聚类,以获得更符合不同目标尺度的锚框,提高多目标定位及其实体分割的精度;在原YOLOv5的骨干网络中添加Coordinate Attention(坐标注意力)模块,以提高模型的特征提取能力;将原YOLOv5网络中的PANet(路径聚合网络)结构替换为BiFPN(双向特征金字塔)结构,实现自上而下与自下而上的深浅层特征双向融合,提高模型对不同尺度目标的整体检测精度。对比实验结果表明:改进后的YOLOv5算法获得了更好的性能,目标检测的mAP达到了92.2%,比改进前的YOLOv5算法提升了8.47%。

Real-time detection of vehicles,pedestrians,and other traffic participants is an important part of information interaction between the autonomous vehicle and the external environment.However,the low accuracy of multi-target detection in complex weather conditions is still a challenge.For this reason,this paper proposes an improved algorithm of YOLOv5 and its application in the multi-target detection of auto-driving systems.In this method,K-means++algorithm is used to cluster the target samples in the data set to obtain anchor frames that are more suitable for different target scales and improve the accuracy of multi-target location and entity segmentation.Coordinate attention module is added to the backbone of the original YOLOv5 to improve the feature extraction capability of the model.The PANet(path aggregation network)structure in the original YOLOv5 network is replaced by the BiFPN(bidirectional feature pyramid)structure to realize the bidirectional fusion of deep and shallow features from top to bottom and from bottom to top,and improves the overall detection accuracy of the algorithm for targets with different scales.The experimental results show that the improved YOLOv5 algorithm achieves better performance,and the mAP of target detection reaches 92.2%,which is 8.47%higher than the results obtained by original YOLOv5 algorithm.