Method

MonoAuxNorm [MonoAuxNorm]


Submitted on 23 Jan. 2024 08:58 by
qie lizhong (Hebei university of technology)

Running time:0.02 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
The confidence of the detection result of the
monocon algorithm is obtained by multiplying the
target confidence and the depth confidence. The
latter makes the comprehensive confidence not
normalized, which is not conducive to determining
the confidence threshold in deployment. In
addition, simple horizontal flipping and
translation data augmentation has limited effect.
We explored the 3D resize augmentation scheme and
normalized the confidence results, which
significantly improved the algorithm performance.
Parameters:
TBD
Latex Bibtex:
TBD

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) 92.56 % 86.62 % 78.73 %
Car (Orientation) 92.29 % 85.93 % 77.78 %
Car (Bird's Eye View) 23.43 % 17.38 % 14.74 %
Pedestrian (Detection) 56.80 % 42.32 % 37.86 %
Pedestrian (Orientation) 50.48 % 37.02 % 32.96 %
Pedestrian (Bird's Eye View) 1.58 % 1.24 % 0.96 %
Cyclist (Detection) 62.78 % 42.37 % 36.88 %
Cyclist (Orientation) 52.62 % 34.71 % 30.22 %
Cyclist (Bird's Eye View) 3.00 % 1.65 % 1.37 %
This table as LaTeX


2D object detection results.
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Orientation estimation 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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Bird's eye view results.
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2D object detection results.
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Orientation estimation results.
This figure as: png eps txt gnuplot



Bird's eye view results.
This figure as: png eps txt gnuplot




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