中圖分類(lèi)號(hào)： TP391,；TP183 文獻(xiàn)標(biāo)識(shí)碼： A DOI：10.16157/j.issn.0258-7998.222802 中文引用格式： 劉俊，鐘國(guó)韻,，黃斯雯,，等. 基于改進(jìn)YOLOv5的車(chē)輛屬性檢測(cè)[J].電子技術(shù)應(yīng)用，2022,，48(7)：19-24，29. 英文引用格式： Liu Jun,，Zhong Guoyun,，Huang Siwen，et al. Vehicle attribute detection based on improved YOLOv5[J]. Application of Electronic Technique,，2022,，48(7)：19-24，29.
Vehicle attribute detection based on improved YOLOv5
Liu Jun,，Zhong Guoyun,，Huang Siwen，Liu Qilin
School of Information Engineering,，East China University of Technology,，Nanchang 330013，China
Abstract： 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.
Key words : vehicle attribute,；object detection,；YOLOv5 algorithm
