Method

DGIST Multi Task CNN [DGIST MT-CNN]


Submitted on 11 Nov. 2020 07:50 by
Jaehyeong Park (DGIST)

Running time:0.09 s
Environment:GPU @ 1.0 Ghz (Python)

Method Description:
The fully convolution CNN(one-stage) model based
2D object detection model.
Applying for multi-task deep learning approach for
different type detection dataset.
Parameters:
Iteration = 20kx8=160k
clip_norm = 0.85
learning_rate = 0.03
back_bone = resnet50
batch_norm_decay = 0.99
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) 95.16 % 93.39 % 85.50 %
Car (Orientation) 39.69 % 38.47 % 35.22 %
Pedestrian (Detection) 88.58 % 79.38 % 74.83 %
Pedestrian (Orientation) 48.67 % 43.26 % 40.74 %
Cyclist (Detection) 86.82 % 72.57 % 63.47 %
Cyclist (Orientation) 36.21 % 31.29 % 27.57 %
This table as LaTeX


2D object detection results.
This figure as: png eps pdf txt gnuplot



Orientation estimation results.
This figure as: png eps pdf txt gnuplot



2D object detection results.
This figure as: png eps pdf txt gnuplot



Orientation estimation results.
This figure as: png eps pdf txt gnuplot



2D object detection results.
This figure as: png eps pdf txt gnuplot



Orientation estimation results.
This figure as: png eps pdf txt gnuplot




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