Method

The Adaptive Feature Fusion Network for Generating High-Level Features Guided by Low-Level Features [AFFN-G]


Submitted on 29 Aug. 2024 10:39 by
紫扬 彭 (湖南大学)

Running time:0.5 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
The Adaptive Feature Fusion Network for Generating
High-Level Features Guided by Low-Level Features
Parameters:
alpha=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) 98.07 % 94.78 % 92.32 %
Car (Orientation) 98.05 % 94.62 % 92.13 %
Car (3D Detection) 90.57 % 81.92 % 79.24 %
Car (Bird's Eye View) 94.46 % 90.61 % 88.12 %
Pedestrian (Detection) 69.06 % 60.99 % 58.89 %
Pedestrian (Orientation) 63.53 % 55.17 % 52.79 %
Pedestrian (3D Detection) 52.89 % 44.63 % 42.36 %
Pedestrian (Bird's Eye View) 58.94 % 51.34 % 49.21 %
Cyclist (Detection) 85.19 % 78.29 % 73.15 %
Cyclist (Orientation) 84.80 % 77.75 % 72.57 %
Cyclist (3D Detection) 80.49 % 67.87 % 61.57 %
Cyclist (Bird's Eye View) 82.10 % 71.57 % 65.37 %
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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