Method

Lite-FPN for SMOKE with ResNet-34 Backbone [Lite-FPN]
[Anonymous Submission]

Submitted on 1 Sep. 2021 17:01 by
[Anonymous Submission]

Running time:0.01 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
We propose a sort of lightweight feature pyramid
network called Lite-FPN and a novel attention loss
to improve the accuracy of keypoint-based monocular
3D object detectors with greater efficiency.
Parameters:
\beta=0.5
Latex Bibtex:

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) 87.04 % 79.65 % 65.56 %
Car (Orientation) 86.85 % 79.13 % 65.03 %
Car (3D Detection) 15.32 % 10.64 % 8.59 %
Car (Bird's Eye View) 26.67 % 17.58 % 14.61 %
Pedestrian (Detection) 50.63 % 36.93 % 32.88 %
Pedestrian (Orientation) 38.84 % 27.90 % 24.60 %
Pedestrian (3D Detection) 6.57 % 4.38 % 3.56 %
Pedestrian (Bird's Eye View) 7.13 % 4.79 % 4.26 %
Cyclist (Detection) 24.40 % 19.17 % 15.68 %
Cyclist (Orientation) 18.06 % 13.74 % 11.15 %
Cyclist (3D Detection) 0.50 % 0.41 % 0.24 %
Cyclist (Bird's Eye View) 0.52 % 0.44 % 0.27 %
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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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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