Method

EVC-CA [ECA]
[Anonymous Submission]

Submitted on 18 Apr. 2024 09:34 by
[Anonymous Submission]

Running time:0.08 s
Environment:GPU @ 1.5 Ghz (Python)

Method Description:
A network architecture has been proposed that
utilizes multi-scale features and channel attention
on the BEV (Bird's Eye View) layer in 3D object
detection to enhance the feature representation of
the BEV layer.
Parameters:
BATCH_SIZE_PER_GPU: 2
NUM_EPOCHS: 50

OPTIMIZER: adam_onecycle
LR: 0.01
WEIGHT_DECAY: 0.01
MOMENTUM: 0.9

MOMS: [0.95, 0.85]
PCT_START: 0.4
DIV_FACTOR: 10
DECAY_STEP_LIST: [35, 45]
LR_DECAY: 0.1
LR_CLIP: 0.0000001

LR_WARMUP: False
WARMUP_EPOCH: 1

GRAD_NORM_CLIP: 10
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) 96.23 % 97.43 % 92.77 %
Car (Orientation) 96.22 % 97.20 % 92.51 %
Car (3D Detection) 88.88 % 81.34 % 78.68 %
Car (Bird's Eye View) 94.10 % 90.70 % 86.04 %
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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