Method

multi task deep learning network for object detect [dgist_multiDetNet]


Submitted on 8 Jun. 2020 02:08 by
Woong-Jae Won (Daegu Gyeongbuk Institute of Science & Technology)

Running time:0.08 s
Environment:GPU Titanx Pascal (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:
Optimizer method: Momentum
learning_rate":0.015
->piecewise_constant
iteration: 100000
multi scale/task training
Auxiliary_task training
handling dontcare region
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) 94.99 % 93.46 % 85.46 %
Car (Orientation) 39.75 % 38.76 % 35.38 %
Pedestrian (Detection) 89.21 % 80.21 % 75.77 %
Pedestrian (Orientation) 49.02 % 43.48 % 40.97 %
Cyclist (Detection) 87.95 % 73.57 % 64.65 %
Cyclist (Orientation) 36.92 % 31.84 % 28.02 %
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




eXTReMe Tracker