Method

YOLOStereo3D with shufflenet-v2 scrach supervised [BKDStereo3D w/o KD]
[Anonymous Submission]

Submitted on 8 Aug. 2023 12:41 by
[Anonymous Submission]

Running time:0.1 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
change YOLOStere3D Resnet 34 backbone to shufflenet
v2, and train with training dataset
Parameters:
learning rate: 0.0001
Latex Bibtex:
inproceedings

Detailed Results

Object detection and orientation estimation results. Results for object detection are given in terms of average precision (AP) and results for joint object detection and orientation estimation are provided in terms of average orientation similarity (AOS).


Benchmark Easy Moderate Hard
Car (Detection) 94.56 % 81.50 % 61.64 %
Car (Orientation) 91.96 % 77.76 % 58.69 %
Car (3D Detection) 56.72 % 32.08 % 23.74 %
Car (Bird's Eye View) 67.38 % 40.69 % 29.98 %
Pedestrian (Detection) 50.58 % 37.02 % 32.92 %
Pedestrian (Orientation) 38.59 % 27.81 % 24.48 %
Pedestrian (3D Detection) 21.47 % 14.92 % 12.96 %
Pedestrian (Bird's Eye View) 23.82 % 16.87 % 14.85 %
This table as LaTeX


2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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2D object detection results.
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Orientation estimation results.
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3D object detection results.
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Bird's eye view results.
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