Louvain-Based Traffic Object Detection for Roadside 4D Millimeter-Wave Radar

Author:

Gong Bowen1ORCID,Sun Jinghang1,Lin Ciyun12ORCID,Liu Hongchao3,Sun Ganghao1ORCID

Affiliation:

1. Department of Traffic Information and Control Engineering, Jilin University, No. 5988, Renmin Street, Changchun 130022, China

2. Jilin Engineering Research Center for Intelligent Transportation System, Changchun 130022, China

3. Department of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USA

Abstract

Object detection is the fundamental task of vision-based sensors in environmental perception and sensing. To leverage the full potential of roadside 4D MMW radars, an innovative traffic detection method is proposed based on their distinctive data characteristics. First, velocity-based filtering and region of interest (ROI) extraction were employed to filter and associate point data by merging the point cloud frames to enhance the point relationship. Then, the Louvain algorithm was used to divide the graph into modularity by converting the point cloud data into graph structure and amplifying the differences with the Gaussian kernel function. Finally, a detection augmentation method is introduced to address the problems of over-clustering and under-clustering based on the object ID characteristics of 4D MMW radar data. The experimental results showed that the proposed method obtained the highest average precision and F1 score: 98.15% and 98.58%, respectively. In addition, the proposed method showcased the lowest over-clustering and under-clustering errors in various traffic scenarios compared with the other detection methods.

Funder

Scientific Research Project of the Education Department of Jilin Province

Qingdao Social Science Planning Research Project

Publisher

MDPI AG

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