Method

Accurate Single Stage Detector Using Recurrent Rolling Convolution [RRC]
https://github.com/xiaohaoChen/rrc_detection

Submitted on 30 Oct. 2018 13:41 by
Jimmy Ren ()

Running time:3.6 s
Environment:GPU @ 2.5 Ghz (C/C++)

Method Description:
High localization accuracy is crucial in many real-
world applications. We propose a novel single stage
end-to-end object detection network (RRC) to
produce high accuracy detection results.
Parameters:
TBD
Latex Bibtex:
@inproceedings{Ren17CVPR,
author = {Jimmy Ren and Xiaohao Chen and Jianbo
Liu and Wenxiu Sun and Jiahao Pang and Qiong Yan
and Yu-Wing Tai and Li Xu},
title = {Accurate Single Stage Detector Using
Recurrent Rolling Convolution},
booktitle = {CVPR},
year = {2017}
}

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) 90.61 % 90.23 % 87.44 %
Pedestrian (Detection) 84.16 % 75.33 % 70.39 %
Cyclist (Detection) 84.96 % 76.49 % 65.46 %
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



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




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