Method

Dilated Detection Backbone [la] [DDB]
[Anonymous Submission]

Submitted on 9 Oct. 2019 17:47 by
[Anonymous Submission]

Running time:0.05 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
We propose a novel Detection Backbone, named DDB,
which uses dilated convolution to replace the
sampling operation in FPN.
Parameters:
learning rate=0.002
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) 93.71 % 91.12 % 87.34 %
Car (Orientation) 93.21 % 90.38 % 86.42 %
Car (3D Detection) 82.45 % 73.49 % 67.82 %
Car (Bird's Eye View) 89.91 % 86.45 % 82.21 %
Pedestrian (Detection) 69.03 % 58.53 % 55.90 %
Pedestrian (Orientation) 52.02 % 43.21 % 40.81 %
Pedestrian (3D Detection) 49.03 % 40.40 % 37.04 %
Pedestrian (Bird's Eye View) 57.68 % 48.35 % 45.44 %
Cyclist (Detection) 78.04 % 61.41 % 55.37 %
Cyclist (Orientation) 75.36 % 58.65 % 52.85 %
Cyclist (3D Detection) 68.83 % 51.38 % 45.15 %
Cyclist (Bird's Eye View) 73.70 % 57.01 % 50.71 %
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.
This figure as: png eps pdf txt gnuplot




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