Computer Engineering and Applications ›› 2025, Vol. 61 ›› Issue (4): 230-240.DOI: 10.3778/j.issn.1002-8331.2309-0283

• Graphics and Image Processing • Previous Articles     Next Articles

Improved Lightweight Multi-Directional License Plate Detection Algorithm of YOLOX

LEI Jingsheng, ZHANG Zhihao, QIAN Xiaohong, WANG Weiran, YANG Shengying   

  1. 1.School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
    2.Information Communication Company, State Grid Shanghai Municipal Electric Power Company, Shanghai 200072, China
  • Online:2025-02-15 Published:2025-02-14

改进YOLOX的轻量级多方向车牌检测算法

雷景生,章志豪,钱小鸿,王巍然,杨胜英   

  1. 1.浙江科技学院 信息与电子工程学院,杭州 310023
    2.国网上海市电力公司信息通信公司,上海 200072

Abstract: In response to the problems of the existing license plate detection algorithms in complex environments, such as poor performance in detecting multi-directional license plates, low real-time capabilities, and excessive model parameters and computational complexity, a lightweight multi-directional license plate detection algorithm based on YOLOX is proposed. By adjusting the number of residual components and using a combination of large convolution kernels and depthwise separable convolutions, the parameter count of the backbone network is reduced. A channel attention mechanism is introduced to effectively extract channel interaction information and reduce noise interference. The feature fusion network is lightweighted by using depthwise separable convolutions and adjusting the expansion ratio. A rotation decoupling head is designed, and an angle prediction branch is added to enable more accurate prediction of the rotation bounding boxes of multi-directional license plates. The rotation IoU loss is used instead of the horizontal IoU loss to improve detection accuracy. Experimental results on the CCPD dataset show that the improved algorithm has the parameter count and computational complexity of 2.38 million and 12.97 GFLOPs, respectively, which are reduced by 45% and 33% compared to YOLOX-tiny. The detection accuracy AP70 is 94.9%, and the detection frame rate is 76.6 FPS. The improved license plate detection model can detect multi-directional license plates in real-time while maintaining high accuracy.

Key words: deep learning, lightweight, YOLOX, object detection, attention mechanism

摘要: 针对现有的车牌检测算法在复杂环境下检测多方向车牌效果不佳、实时性差以及模型参数量和计算量过大等问题,提出了一种基于YOLOX的轻量级多方向车牌检测算法。通过调整残差组件的数量,并采用大卷积核结合深度可分离卷积的方法,降低了主干网络的参数量。引入通道注意力机制,以有效提取通道交互信息,减少噪声的干扰。使用深度可分离卷积和调整扩展率的方法对特征融合网络进行轻量化处理。设计了旋转解耦头,通过添加角度预测分支,使其能够更精准地预测多方向车牌的旋转边界框。采用旋转IoU损失代替水平IoU损失,提高检测的准确性。CCPD数据集上的实验结果表明,改进算法的参数量和计算复杂度分别为2.38×106和12.97?GFLOPs,相较于YOLOX-tiny分别减少了45%和33%,检测精度AP70为94.9%,每秒检测帧数为76.6?FPS。改进后的车牌检测模型能够在保持高精度的同时实时检测多方向车牌。

关键词: 深度学习, 轻量化, YOLOX, 目标检测, 注意力机制