Vehicle Detection Based on Fusion of Millimeter-wave Radar and Machine Vision

被引：0

作者：

Zhang B. ^{[1
,2
]}

Zhan Y. ^{[1
,2
]}

Pan D. ^{[3
]}

Cheng J. ^{[1
,2
]}

Song W. ^{[1
,2
]}

Liu W. ^{[1
,2
]}

机构：

[1] School of Automobile and Traffic Engineering, Hefei University of Technology, Hefei

[2] Anhui Engineering Laboratory of Intelligent Automobile, Hefei

[3] Hefei Changan Automobile Co., Ltd., Hefei

来源：

Zhan, Yehui (2018170716@mail.hfut.edu.cn) | 1600年 / SAE-China卷 / 43期

关键词：

Millimeter wave radar; Multi-target tracking; Sensor fusion; Vehicle detection; YOLO algorithm;

D O I：

10.19562/j.chinasae.qcgc.2021.04.004

中图分类号：

学科分类号：

摘要：

Aiming at the defects of poor identification effects and prone to be disturbed when using traditional single sensor in vehicle detection, a vehicle detection method based on the fusion of millimeter wave radar and machine vision is propose in this paper. Firstly, the radar data is processed by using hierarchical clustering algorithm with invalid targets filtered out, and the improved YOLO v2 algorithm is adopted to reduce the missed detection rate and increase the detection speed. Then, the intersection-over-union (IoU) of target detection and the global nearest neighbor data association algorithm are utilized to achieve multi-sensor data fusion. Finally, the extended Kalman filter algorithm is employed for target tracking, with the final result obtained. The results of real vehicle test show that the results of vehicle identification with the method proposed is better than that with single sensor, and has good recognition effects under various road conditions. © 2021, Society of Automotive Engineers of China. All right reserved.

引用

页码：478 / 484

页数：6

共 11 条

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