Method

Monocular 3D Object Detection Utilizing Auxiliary Learning with Deformable Convolution [MonoAD]


Submitted on 13 Sep. 2022 04:45 by
Roderick Chen (NTUST)

Running time:0.03 s
Environment:GPU @ 2.5 Ghz (Python)

Method Description:
The proposed model utilizes auxiliary regression heads
for learning additional knowledge without external data
involved in the training. Also, the flexible deformable
convolution mechanism and novel mish activation
function are applied to facilitate the learning of the
3D conceptual features.
Parameters:
Epochs: 400
Batch Size: 8
Optimizer: AdamW
Scheduler: OneCycle
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) 94.43 % 88.99 % 76.76 %
Car (Orientation) 94.35 % 88.61 % 76.35 %
Car (3D Detection) 25.35 % 17.28 % 14.58 %
Car (Bird's Eye View) 33.33 % 22.70 % 20.48 %
Pedestrian (Detection) 56.38 % 40.39 % 36.17 %
Pedestrian (Orientation) 51.18 % 36.38 % 32.41 %
Pedestrian (3D Detection) 13.85 % 8.84 % 7.38 %
Pedestrian (Bird's Eye View) 14.65 % 9.44 % 8.60 %
Cyclist (Detection) 66.02 % 44.87 % 39.94 %
Cyclist (Orientation) 52.70 % 35.64 % 31.90 %
Cyclist (3D Detection) 6.59 % 4.22 % 3.52 %
Cyclist (Bird's Eye View) 8.13 % 4.85 % 4.71 %
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.
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