Advanced Point Cloud Techniques for Improved 3D Object Detection: A Study on DBSCAN, Attention, and Downsampling
To address the challenges of limited detection precision and insufficient segmentation of small to medium-sized objects in dynamic and complex scenarios, such as the dense intermingling of pedestrians, vehicles, and various obstacles in urban environments, we propose an enhanced methodology. Firstly...
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| Format: | Article |
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MDPI AG
2024-11-01
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| Series: | World Electric Vehicle Journal |
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| Online Access: | https://www.mdpi.com/2032-6653/15/11/527 |
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| author | Wenqiang Zhang Xiang Dong Jingjing Cheng Shuo Wang |
| author_facet | Wenqiang Zhang Xiang Dong Jingjing Cheng Shuo Wang |
| author_sort | Wenqiang Zhang |
| collection | DOAJ |
| description | To address the challenges of limited detection precision and insufficient segmentation of small to medium-sized objects in dynamic and complex scenarios, such as the dense intermingling of pedestrians, vehicles, and various obstacles in urban environments, we propose an enhanced methodology. Firstly, we integrated a point cloud processing module utilizing the DBSCAN clustering algorithm to effectively segment and extract critical features from the point cloud data. Secondly, we introduced a fusion attention mechanism that significantly improves the network’s capability to capture both global and local features, thereby enhancing object detection performance in complex environments. Finally, we incorporated a CSPNet downsampling module, which substantially boosts the network’s overall performance and processing speed while reducing computational costs through advanced feature map segmentation and fusion techniques. The proposed method was evaluated using the KITTI dataset. Under moderate difficulty, the BEV mAP for detecting cars, pedestrians, and cyclists achieved 87.74%, 55.07%, and 67.78%, reflecting improvements of 1.64%, 5.84%, and 5.53% over PointPillars. For 3D mAP, the detection accuracy for cars, pedestrians, and cyclists reached 77.90%, 49.22%, and 62.10%, with improvements of 2.91%, 5.69%, and 3.03% compared to PointPillars. |
| format | Article |
| id | doaj-art-62b0d2d061cd493b804b2a9bac3e5584 |
| institution | OA Journals |
| issn | 2032-6653 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | World Electric Vehicle Journal |
| spelling | doaj-art-62b0d2d061cd493b804b2a9bac3e55842025-08-20T01:53:56ZengMDPI AGWorld Electric Vehicle Journal2032-66532024-11-01151152710.3390/wevj15110527Advanced Point Cloud Techniques for Improved 3D Object Detection: A Study on DBSCAN, Attention, and DownsamplingWenqiang Zhang0Xiang Dong1Jingjing Cheng2Shuo Wang3School of Electrical Engineering and Automation, Anhui University, Hefei 230601, ChinaSchool of Electrical Engineering and Automation, Anhui University, Hefei 230601, ChinaSchool of Electrical Engineering and Automation, Anhui University, Hefei 230601, ChinaState Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, ChinaTo address the challenges of limited detection precision and insufficient segmentation of small to medium-sized objects in dynamic and complex scenarios, such as the dense intermingling of pedestrians, vehicles, and various obstacles in urban environments, we propose an enhanced methodology. Firstly, we integrated a point cloud processing module utilizing the DBSCAN clustering algorithm to effectively segment and extract critical features from the point cloud data. Secondly, we introduced a fusion attention mechanism that significantly improves the network’s capability to capture both global and local features, thereby enhancing object detection performance in complex environments. Finally, we incorporated a CSPNet downsampling module, which substantially boosts the network’s overall performance and processing speed while reducing computational costs through advanced feature map segmentation and fusion techniques. The proposed method was evaluated using the KITTI dataset. Under moderate difficulty, the BEV mAP for detecting cars, pedestrians, and cyclists achieved 87.74%, 55.07%, and 67.78%, reflecting improvements of 1.64%, 5.84%, and 5.53% over PointPillars. For 3D mAP, the detection accuracy for cars, pedestrians, and cyclists reached 77.90%, 49.22%, and 62.10%, with improvements of 2.91%, 5.69%, and 3.03% compared to PointPillars.https://www.mdpi.com/2032-6653/15/11/527autonomous vehiclesattention mechanismDBSCANCSPNetPointPillars |
| spellingShingle | Wenqiang Zhang Xiang Dong Jingjing Cheng Shuo Wang Advanced Point Cloud Techniques for Improved 3D Object Detection: A Study on DBSCAN, Attention, and Downsampling World Electric Vehicle Journal autonomous vehicles attention mechanism DBSCAN CSPNet PointPillars |
| title | Advanced Point Cloud Techniques for Improved 3D Object Detection: A Study on DBSCAN, Attention, and Downsampling |
| title_full | Advanced Point Cloud Techniques for Improved 3D Object Detection: A Study on DBSCAN, Attention, and Downsampling |
| title_fullStr | Advanced Point Cloud Techniques for Improved 3D Object Detection: A Study on DBSCAN, Attention, and Downsampling |
| title_full_unstemmed | Advanced Point Cloud Techniques for Improved 3D Object Detection: A Study on DBSCAN, Attention, and Downsampling |
| title_short | Advanced Point Cloud Techniques for Improved 3D Object Detection: A Study on DBSCAN, Attention, and Downsampling |
| title_sort | advanced point cloud techniques for improved 3d object detection a study on dbscan attention and downsampling |
| topic | autonomous vehicles attention mechanism DBSCAN CSPNet PointPillars |
| url | https://www.mdpi.com/2032-6653/15/11/527 |
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