Method

Aston-EAS [Aston-EAS]


Submitted on 7 May. 2019 15:47 by
jian wei (aston university)

Running time:0.24 s
Environment:GPU @ 2.5 Ghz (Python + C/C++)

Method Description:
Three enhancements including deconvolution, anchor setting and soft-NMS are proposed on a multiple
scale CNN network model.
Parameters:
/
Latex Bibtex:
@article{wei2019enhanced,
title={Enhanced Object Detection With Deep Convolutional Neural Networks for Advanced Driving Assistance},
author={Wei, Jian and He, Jianhua and Zhou, Yi and Chen, Kai and Tang, Zuoyin and Xiong, Zhiliang},
journal={IEEE Transactions on Intelligent Transportation Systems},
year={2019},
publisher={IEEE}
}

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) 90.49 % 89.64 % 77.95 %
Pedestrian (Detection) 85.12 % 74.52 % 69.35 %
This table as LaTeX


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



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




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