Method

Multi-level adaptive feature fusion [MLAFF]
[Anonymous Submission]

Submitted on 7 Apr. 2023 10:38 by
[Anonymous Submission]

Running time:0.39 s
Environment:2 cores @ 2.5 Ghz (Python)

Method Description:
Multi-level adaptive feature fusion mainly consists
of three sub-modules, one is feature mapping module,
the other is multi-level feature extraction, and the
third is adaptive weighted fusion of extracted
multi-level features.
Parameters:
α = 0.25,γ = 2; β1 = 1.0, β2 = 2.0,β3 = 0.2
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) 95.39 % 92.54 % 87.77 %
Car (Orientation) 95.17 % 91.84 % 86.94 %
Car (3D Detection) 83.75 % 74.64 % 68.32 %
Car (Bird's Eye View) 91.02 % 85.84 % 80.93 %
Pedestrian (Detection) 67.96 % 55.22 % 53.15 %
Pedestrian (Orientation) 58.34 % 46.92 % 44.82 %
Pedestrian (3D Detection) 47.81 % 38.41 % 36.15 %
Pedestrian (Bird's Eye View) 53.43 % 43.67 % 41.41 %
Cyclist (Detection) 80.28 % 64.41 % 58.52 %
Cyclist (Orientation) 77.64 % 62.03 % 56.00 %
Cyclist (3D Detection) 61.63 % 45.95 % 40.87 %
Cyclist (Bird's Eye View) 67.41 % 51.69 % 45.99 %
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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