Laser & Optoelectronics Progress, Volume. 60, Issue 16, 1612002(2023)
Multiscale Monocular Three-Dimensional Object Detection Algorithm Incorporating Instance Depth
Figures & Tables(7)
Table 1. Performance of the Car category on the KITTI test set
View tableTable 1. Performance of the Car category on the KITTI test set
Method Extra data AP40（3D@RIOU≥0.7） AP40（BEV@RIOU≥0.7） Easy Moderate Hard Easy Moderate Hard AM3D［ 24 ］Depth 16.50 10.74 9.52 25.03 17.32 14.91 PatchNet［ 4 ］Depth 15.68 11.12 10.17 22.97 16.86 14.97 DDMP-3D［ 5 ］Depth 19.71 12.78 9.80 28.08 17.89 13.44 Reference［ 25 ］Depth 20.28 13.12 9.56 Kinematic3D［ 26 ］Multi-frames 19.07 12.72 9.17 26.69 17.52 13.10 CaDDN［ 27 ］LiDAR 19.17 13.41 11.46 27.94 18.91 17.19 MonoRUn［ 28 ］LiDAR 19.65 12.30 10.58 27.94 17.34 15.24 MonoGRNet［ 11 ］None 9.61 5.74 4.25 18.19 11.17 8.73 MonoDIS［ 23 ］None 10.37 7.94 6.40 17.23 13.19 11.12 Reference［ 29 ］None 20.89 14.49 12.19 29.57 20.77 17.88 MonoPair［ 6 ］None 13.04 9.99 8.65 19.28 14.83 12.89 FADNet［ 30 ］None 16.37 9.92 8.05 23.00 14.22 12.56 MonoDLE［ 9 ］None 17.23 12.26 10.29 24.79 18.89 16.00 MonoGround［ 31 ］None 19.48 14.36 12.62 30.07 20.47 17.74 MonoFlex［ 7 ］None 19.94 13.89 12.07 28.23 19.75 16.89 MonoEF［ 32 ］None 21.29 13.87 11.71 29.03 19.70 17.26 Reference［ 33 ］None 21.65 13.25 9.91 29.81 17.98 13.08 GUPNet［ 8 ］None 22.26 15.02 13.12 30.29 21.19 18.20 MonoCon［ 34 ］None 22.50 16.49 13.95 31.12 22.10 19.00 Proposed method None 22.50 16.19 13.49 32.44 22.97 19.82 Improvement Depth +2.22 +3.07 +3.32 +4.36 +5.08 +4.85 Multi-frames +3.43 +3.47 +4.78 +5.75 +5.45 +6.72 LiDAR +2.85 +2.78 +2.03 +4.5 +4.06 +2.63 None +0 -0.3 -0.46 +1.32 +0.87 +0.82
Table 2. Performance of the Car category on the KITTI validation set
View tableTable 2. Performance of the Car category on the KITTI validation set
Method AP40 /%（3D@RIOU=0.7） AP40 /%（BEV@RIOU=0.7） AP40 /%（3D@RIOU=0.5） AP40 /%（BEV@RIOU=0.5） Runtime /ms Easy Moderate Hard Easy Moderate Hard Easy Moderate Hard Easy Moderate Hard Improvement +5.14 +4.13 +3.31 +5.25 +3.98 +3.03 +8.81 +4.07 +4.73 +7.41 +4.49 +4.19 CenterNet［ 10 ］0.60 0.66 0.77 3.46 3.31 3.21 20.00 17.50 15.57 34.36 27.91 24.65 MonoGRNet 11.90 7.56 5.76 19.72 12.81 10.15 47.59 32.28 25.50 48.53 35.94 28.59 60 MonoDIS 11.06 7.60 6.37 18.45 12.58 10.66 M3D-RPN 14.53 11.07 8.65 20.85 15.62 11.88 48.53 35.94 28.59 53.35 39.60 31.76 161 MonoPair 16.28 12.30 10.42 24.12 18.17 15.76 55.38 42.39 37.99 61.06 47.63 41.92 57 MonoDLE 17.45 13.66 11.68 24.97 19.33 17.01 55.41 43.42 37.81 60.73 46.87 41.89 40 Proposed method 22.59 17.79 14.99 30.22 23.31 20.04 64.22 47.49 42.72 68.47 52.12 46.11 45
Table 3. Adding Performance Comparison of Different Modules on KITTI validation set
View tableTable 3. Adding Performance Comparison of Different Modules on KITTI validation set
Method AP40（3D） AP40（BEV） Easy Moderate Hard Easy Moderate Hard Improvement +5.14 +4.13 +3.31 +5.25 +3.98 +3.03 Baseline 17.45 13.66 11.68 24.97 19.33 17.01 +ASPP 18.04 14.72 12.47 25.60 20.84 18.20 +PSP 18.98 14.73 12.38 25.61 20.75 18.06 +MSS 20.48 15.89 14.06 28.58 22.41 19.46 +IDLM 21.76 16.19 14.24 28.71 22.44 19.43 +MSS+IDLM 22.59 17.79 14.99 30.22 23.31 20.04
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Fengsui Wang, Lei Xiong, Yaping Qian. Multiscale Monocular Three-Dimensional Object Detection Algorithm Incorporating Instance Depth[J]. Laser & Optoelectronics Progress, 2023, 60(16): 1612002
Paper Information
Category: Instrumentation, Measurement and Metrology
Received: Sep. 26, 2022
Accepted: Oct. 24, 2022
Published Online: Aug. 15, 2023
The Author Email: Wang Fengsui (fswang@ahpu.edu.cn)
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