摘要
针对目前车辆实时检测中存在定位不准确、检测精度低等问题,采用了一种以Darknet-53为骨架网络的YOLOv3车辆检测算法,将该算法模型在标准数据集Pascal-VOC2012上进行训练,以拍摄的西安南二环路的图片作为测试集进行测试。实验结果表明,YOLOv3算法的检测精度达到84.9%,相比于SSD算法,其检测精度提高了11.3%,检测速度提高了3.8 f/s。因此YOLOv3算法检测精度更好,检测速度更快,能准确地检测出图像中的车辆信息,满足车辆实时检测的要求。
In view of the current real-time detection of vehicles,there are problems such as inaccurate positioning and low detection accuracy.This paper uses a YOLOv3 vehicle detection algorithm with Darknet-53 as the skeleton network.The algorithm model is trained on the standard data set Pascal-VOC2012,and the pictures of Xi′an South Second Ring Road are taken as the test set for testing.Experimental results show that the detection accuracy of YOLOv3 algorithm reaches 84.9%,which is 11.3%higher than that of SSD algorithm.The detection speed has also increased by 3.8 f/s.Therefore,YOLOv3 algorithm has better detection accuracy and faster detection speed,can accurately detect the vehicle information in the image,and meet the requirements of real-time vehicle detection.
作者
苏欣欣
郭元术
李妮妮
Su Xinxin;Guo Yuanshu;Li Nini(School of Information Engineering,Chang′an University,Xi′an 710064,China)
出处
《信息技术与网络安全》
2021年第6期28-32,共5页
Information Technology and Network Security
基金
河南省交通运输厅重点项目(220024140173)。
