基于YOLOV3改进的实时车辆检测方法  

Improved Real-Time Vehicle Detection Method Based on YOLOV3

作　　者：李汉冰 徐春阳 胡超超 Li Hanbing;Xu Chunyang;Hu Chaochao(Schood of Automotire and Traffic Engineering,Jjiangsu Universiy,Zhenjiang,Jjiangsu 212013,China)

机构地区：[1]江苏大学汽车与交通工程学院,江苏镇江212013

出　　处：《激光与光电子学进展》2020年第10期324-330,共7页Laser & Optoelectronics Progress

基　　金：国家自然科学基金(51275212)。

摘　　要：针对原始YOLOV3目标检测算法在车辆检测任务中存在的实时性不高的问题,提出了一种改进的车辆检测模型。该模型使用反残差网络作为基础特征提取层,以减少参数量,降低计算复杂度,解决梯度消失和梯度爆炸问题。并且使用组归一化降低批量大小对模型准确性的影响,同时用软化非极大值抑制降低漏检率,使用Focalloss改进损失函数,使模型在训练时聚焦于难分类样本。改进后的模型参数量为YOLOV3的36.23%,每帧检测时间较YOLOV3降低了13.8ms,平均类别精度提高了1.15%。结果表明,本文算法兼顾实时性和准确性,为车辆的实时性检测提供参考。Aiming at the problem of low real-time performance of original YOLOV3 target detection algorithm in vehicle detection tasks,this paper proposes an improved vehicle detection model.In order to reduce the number of parameters,reduce the computational complexity,and solve the problem of gradient disappearance and gradient explosion,the model uses the inverted residual network as the basic feature extraction layer.In addition,group normalization is used to reduce the impact of batch size on the accuracy of the model.At the same time,softening non maximum suppression is used to reduce the rate of missed detection.Finally,the Focal-loss is used to improve the loss function so that the model focuses on the difficult-to-classify samples in the process of training.The parameter amount of the improved model is 36.23%of YOLOV3 model.The detection time per frame is reduced by 13.8 ms compared with YOLOV3,and the average category accuracy is improved by 1.15%.The results show that the proposed algorithm ensures both real-time performance and accuracy,and providing a reference for realtime detection of vehicles.

关 键 词：机器视觉 车辆 目标检测 YOLOV3 反残差网络 实时检测 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]