基于改进YOLOv5的车辆属性检测

Vehicle attribute detection based on improved YOLOv5

车辆属性检测是一个基础任务,其属性检测结果可以被应用到很多下游的交通视觉任务。提出了一种基于YOLOv5的车辆属性检测改进算法。针对检测目标较小的问题,加入了卷积注意力模块,让网络模型把更多的注意力放在小目标对象上;针对数据集样本种类较少的问题,改进了YOLOv5的马赛克数据增强方式;使用自门控激活函数Swish,起到抑制噪声、加快收敛速度并提升模型鲁棒性的作用。此外,还在公开车辆数据集VeRi-776的基础上进行了详细的车辆属性标注,构建了一个车辆属性数据集。实验结果表明,改进后的算法比原始YOLOv5的平均精确率提升了4.6%,能够准确地检测到车辆图像的通用属性,可以供下游任务使用。

Vehicle attribute detection is a basic task,which can be applied to many downstream traffic vision tasks.This paper presents an improved vehicle attribute detection algorithm based on YOLOv5.Aiming at the problem of small target detection,this paper adds the convolution attention module to make the network model pay more attention to the small target object.Aiming at the problem of less sample types of the dataset,this paper improves the mosaic data enhancement method of YOLOv5.The self-gated activation function Swish is used to suppress noise,accelerate convergence speed,and improve the robustness of the model.In addition,this paper also makes a detailed vehicle attribute labeling based on the public vehicle dataset VeRi-776,and constructs a vehicle attribute dataset.The experimental results show that the average accuracy of the improved algorithm is 4.6%higher than that of the original YOLOv5,which can accurately detect the general attributes of vehicle images and can be used for downstream tasks.