Louvain-Based Traffic Object Detection for Roadside 4D Millimeter-Wave Radar
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/2/366 |
| _version_ | 1827371174199296000 |
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| author | Bowen Gong Jinghang Sun Ciyun Lin Hongchao Liu Ganghao Sun |
| author_facet | Bowen Gong Jinghang Sun Ciyun Lin Hongchao Liu Ganghao Sun |
| author_sort | Bowen Gong |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T10:35:25Z |
| format | Article |
| id | doaj.art-65467d3edc494522b07e1cb0e4b864e7 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-08T10:35:25Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-65467d3edc494522b07e1cb0e4b864e72024-01-26T18:19:24ZengMDPI AGRemote Sensing2072-42922024-01-0116236610.3390/rs16020366Louvain-Based Traffic Object Detection for Roadside 4D Millimeter-Wave RadarBowen Gong0Jinghang Sun1Ciyun Lin2Hongchao Liu3Ganghao Sun4Department of Traffic Information and Control Engineering, Jilin University, No. 5988, Renmin Street, Changchun 130022, ChinaDepartment of Traffic Information and Control Engineering, Jilin University, No. 5988, Renmin Street, Changchun 130022, ChinaDepartment of Traffic Information and Control Engineering, Jilin University, No. 5988, Renmin Street, Changchun 130022, ChinaDepartment of Civil, Environmental and Construction Engineering, Texas Tech University, Lubbock, TX 79409, USADepartment of Traffic Information and Control Engineering, Jilin University, No. 5988, Renmin Street, Changchun 130022, ChinaObject 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.https://www.mdpi.com/2072-4292/16/2/366roadside 4D millimeter-wave radartraffic object detectionLouvainpoint cloud data processing |
| spellingShingle | Bowen Gong Jinghang Sun Ciyun Lin Hongchao Liu Ganghao Sun Louvain-Based Traffic Object Detection for Roadside 4D Millimeter-Wave Radar Remote Sensing roadside 4D millimeter-wave radar traffic object detection Louvain point cloud data processing |
| title | Louvain-Based Traffic Object Detection for Roadside 4D Millimeter-Wave Radar |
| title_full | Louvain-Based Traffic Object Detection for Roadside 4D Millimeter-Wave Radar |
| title_fullStr | Louvain-Based Traffic Object Detection for Roadside 4D Millimeter-Wave Radar |
| title_full_unstemmed | Louvain-Based Traffic Object Detection for Roadside 4D Millimeter-Wave Radar |
| title_short | Louvain-Based Traffic Object Detection for Roadside 4D Millimeter-Wave Radar |
| title_sort | louvain based traffic object detection for roadside 4d millimeter wave radar |
| topic | roadside 4D millimeter-wave radar traffic object detection Louvain point cloud data processing |
| url | https://www.mdpi.com/2072-4292/16/2/366 |
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