Method

A single shot anchor free Detector based on fusion-painting and visibility feature [FPV-SSD]
[Anonymous Submission]

Submitted on 18 Nov. 2021 05:00 by
[Anonymous Submission]

Running time:0.03 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
we propose a novel feature fusion network that
combines both visibility feature and image semantic
feature and study an efficient and quick fusion
strategy to fuse them.
Parameters:
learning rate=0.03
Latex Bibtex:
not published

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) 96.92 % 94.66 % 91.78 %
Car (Orientation) 96.89 % 94.48 % 91.52 %
Car (3D Detection) 87.72 % 80.34 % 75.40 %
Car (Bird's Eye View) 91.45 % 89.93 % 85.21 %
Pedestrian (Detection) 62.00 % 53.10 % 50.67 %
Pedestrian (Orientation) 59.60 % 50.15 % 47.46 %
Pedestrian (3D Detection) 45.83 % 38.45 % 36.03 %
Pedestrian (Bird's Eye View) 50.37 % 43.19 % 40.95 %
Cyclist (Detection) 83.30 % 75.33 % 68.10 %
Cyclist (Orientation) 83.16 % 74.93 % 67.66 %
Cyclist (3D Detection) 78.36 % 64.40 % 56.72 %
Cyclist (Bird's Eye View) 80.32 % 68.15 % 60.51 %
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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