基于改进的YOLO v3车辆检测方法

Vehicle Detection Method Based on Improved YOLO v3

为了有效地解决传统车辆检测算法中存在的泛化能力差、识别率不高的问题,提出了一种基于改进YOLO v3的车辆检测算法。改进的车辆算法对原YOLO v3中的模型进行剪枝处理,采用Darknet-53网络结构提取特征,同时结合回归损失函数GIOU算法对检测精度进行提高。在运用K-means++聚类分析算法处理数据基础上,运用所提出的改进YOLO v3算法,基于COCO数据集进行了网络的训练、测试和验证。试验结果表明,改进后的YOLO v3算法在车辆检测上的泛化能力得到提升,并兼具速度优势。

In order to effectively solve the problems of poor generalization ability and low recognition rate in traditional vehicle detection algorithm,this paper proposes a vehicle detection algorithm based on improved YOLO v3.The improved vehicle algorithm prunes the original model in YOLO v3,extracts features by using the structure of Darknet-53 network,and improves the detection accuracy by combining the regression loss function GIOU algorithm.K-means++clustering analysis algorithm is used to process data,and the improved YOLO v3 algorithm is used to train,test and verify the network based on COCO data set.The experimental results show that the generalization ability of the improved YOLO v3 algorithm in vehicle detection is improved,and has speed advantage.