面向路侧视角目标检测的轻量级YOLOv7-R算法

Lightweight YOLOv7-R Algorithm for Road-Side View Target Detection

针对V2X中的路侧感知单元在检测过程中,模型部署困难的问题、被测目标所呈现的多尺度问题及目标之间遮挡问题,提出了一种基于YOLOv7算法的轻量级检测算法YOLOv7-R。首先使用改进的EfficientNetv2-s重新构建YOLOv7的主干网络,减小模型参数量,提高模型的推理速度。其次,采用CA坐标注意力机制,保留精确的位置信息,加强模型对多尺度目标的检测性能;同时采用Focal-EIoU损失函数,提升算法精度。最后,在预处理阶段使用GridMask数据增强,提升算法对被遮挡目标的学习能力。实验结果表明:相较于基线算法YOLOv7,该算法在DAIR-V2X-I数据集上的map@0.5和map@0.5:0.95分别提高了3%与4.8%,检测速率达到了96.3 f/s,从而在满足轻量化要求的同时得到更优的检测精度,有效地实现了路侧单元对交通参与者的检测任务。

A lightweight detection algorithm YOLOV7-R based on the YOLOv7 algorithm is proposed to solve the problems of model deployment difficulty,multi-scale problem of the measured target and occlusion prob‐lem between targets in the detection process of the road side sensing unit in V2X.Firstly,the backbone of YOLOv7 is rebuilt using the improved EfficientNetv2-s to reduce the model parameters and improve the model detection speed.Secondly,CA coordinate attention mechanism is adopted to retain accurate location information to enhance the performance of the model for multi-scale targets.At the same time,Focal-EIoU loss function is utilized to en‐hance the accuracy of the algorithm.Finally,GridMask image enhancement is used in the pre-processing stage to improve the learning ability of the algorithm for the blocked target.The experimental results show that compared with the baseline algorithm YOLOv7,the map@0.5 and map@0.5:0.95 value of the proposed algorithm on the DAIR-V2X-I dataset is increased by 3%and 4.8%,respectively,with the detection rate reaching 96.3 f/s,which can meet the requirements of lightweight and obtain better detection accuracy,and effectively implement the detec‐tion task of the road side unit for traffic participants.