Laser & Optoelectronics Progress, Volume. 61, Issue 8, 0812007(2024)
Laser Radar 3D Target Detection Based on Improved PointPillars
Figures & Tables(12)
Fig. 2. Structure of the pillar feature network of the original PointPillars model
Fig. 6. 3D object detection renderings and 2D images of the proposed algorithm on different scenes. (a) Scene one; (b) scene two; (c) scene three; (d) scene four
Fig. 7. Comparison of detection performance between proposed algorithm and PointPillars algorithm: complex scenes
Fig. 8. Comparison of detection performance between proposed algorithm and PointPillars algorithm: long-distance scenes
Table 1. Comparison of mAP for different algorithms under car category
View tableTable 1. Comparison of mAP for different algorithms under car category
Model AP /% mAP /% Easy Moderate Hard VoxelNet 87.93 75.37 73.21 78.84 SECOND 88.61 78.62 77.22 81.48 PointPillars 87.50 77.01 74.77 79.76 3D-GIoU 87.83 77.91 78.84 82.60 TANet 88.17 77.75 75.31 80.41 PointRCNN 89.01 78.77 78.10 81.96 Point-GNN 89.33 79.47 78.29 82.36 Part-A2 89.56 79.41 78.84 82.60 Ours 89.28 79.56 79.62 82.82
Table 2. Comparison of mAP for different algorithms under the pedistrian category
View tableTable 2. Comparison of mAP for different algorithms under the pedistrian category
Model AP /% mAP /% Easy Moderate Hard VoxelNet 67.81 63.52 58.87 63.40 SECOND 56.00 50.02 43.64 49.89 PointPillars 66.73 61.06 56.50 61.43 3D-GIoU 67.23 59.58 52.69 59.83 TANet 70.80 63.45 58.22 64.16 PointRCNN 62.69 55.36 51.60 56.55 Point-GNN 61.92 53.77 50.14 55.28 Part-A2 65.69 60.05 55.45 60.40 Ours 71.33 63.75 58.63 64.57
Table 3. Comparison of mAP for different algorithms under the cyclist category
View tableTable 3. Comparison of mAP for different algorithms under the cyclist category
Model AP /% mAP /% Easy Moderate Hard VoxelNet 77.69 58.72 51.63 62.68 SECOND 80.97 63.43 56.67 67.02 PointPillars 83.65 63.40 59.71 68.92 3D-GIoU 83.32 64.69 63.51 70.51 TANet 85.21 65.29 61.57 70.69 PointRCNN 84.48 65.37 59.83 69.89 Point-GNN 86.60 67.48 62.58 72.22 Part-A2 85.50 68.90 64.53 72.98 Ours 87.88 68.75 64.26 73.63
Table 4. Results of ablation experiment
View tableTable 4. Results of ablation experiment
Method Average
pooling
Attention
pooling
ConvNeXt AP /% mAP /% FPS /（frame/s） Car Pedestrian Cyclist Baseline 79.76 61.43 68.92 70.04 42.2 Experiment 1 √ 80.31 62.03 70.90 71.08 38.3 Experiment 2 √ 80.54 62.43 71.47 71.48 36.4 Experiment 3 √ √ 81.26 62.78 72.39 72.14 33.1 Ours √ √ √ 82.82 64.57 73.63 73.67 26.1
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Feng Tian, Chao Liu, Fang Liu, Wenwen Jiang, Xin Xu, Ling Zhao. Laser Radar 3D Target Detection Based on Improved PointPillars[J]. Laser & Optoelectronics Progress, 2024, 61(8): 0812007
Paper Information
Category: Instrumentation, Measurement and Metrology
Received: Jun. 8, 2023
Accepted: Jul. 24, 2023
Published Online: Apr. 2, 2024
The Author Email: Fang Liu (lfliufang1983@126.com)
CSTR:32186.14.LOP231493
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