Learning Rotation Sensitive Neural Network for Deformed Objects' Detection in Fisheye Images

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Standard

Learning Rotation Sensitive Neural Network for Deformed Objects' Detection in Fisheye Images. / Chen, Zhen; Georgiadis, Anthimos.
2019 4th International Conference on Robotics and Automation Engineering (ICRAE 2019): November 22-24, 2019, Singapore . Piscataway: IEEE - Institute of Electrical and Electronics Engineers Inc., 2019. p. 125-129 9043800 (International Conference on Robotics and Automation Engineering, ICRAE ; Vol. 4).

Research output: Contributions to collected editions/worksArticle in conference proceedingsResearchpeer-review

Harvard

Chen, Z & Georgiadis, A 2019, Learning Rotation Sensitive Neural Network for Deformed Objects' Detection in Fisheye Images. in 2019 4th International Conference on Robotics and Automation Engineering (ICRAE 2019): November 22-24, 2019, Singapore ., 9043800, International Conference on Robotics and Automation Engineering, ICRAE , vol. 4, IEEE - Institute of Electrical and Electronics Engineers Inc., Piscataway, pp. 125-129, 4th International Conference on Robotics and Automation Engineering, ICRAE 2019, Singapore, Singapore, 22.11.19. https://doi.org/10.1109/ICRAE48301.2019.9043800

APA

Chen, Z., & Georgiadis, A. (2019). Learning Rotation Sensitive Neural Network for Deformed Objects' Detection in Fisheye Images. In 2019 4th International Conference on Robotics and Automation Engineering (ICRAE 2019): November 22-24, 2019, Singapore (pp. 125-129). Article 9043800 (International Conference on Robotics and Automation Engineering, ICRAE ; Vol. 4). IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRAE48301.2019.9043800

Vancouver

Chen Z, Georgiadis A. Learning Rotation Sensitive Neural Network for Deformed Objects' Detection in Fisheye Images. In 2019 4th International Conference on Robotics and Automation Engineering (ICRAE 2019): November 22-24, 2019, Singapore . Piscataway: IEEE - Institute of Electrical and Electronics Engineers Inc. 2019. p. 125-129. 9043800. (International Conference on Robotics and Automation Engineering, ICRAE ). doi: 10.1109/ICRAE48301.2019.9043800

Bibtex

@inbook{2b05183077a3424685998d0eab3b31a6,
title = "Learning Rotation Sensitive Neural Network for Deformed Objects' Detection in Fisheye Images",
abstract = "Object detection plays a significant role in an intelligent system equipped with a fisheye camera. The fisheye image captures a wide field-of-view but deforms in the radial direction. The deformation changes the relative angle between the edge of objects and the image. Therefore, a horizontal bounding box cannot perform an accurate description of an object's location and dimension in advanced neural network training. In this paper, we build a rotation sensitive neural network targeting to realize one-stage regression on the fisheye image detection. The oriented bounding box is applied in the object's description and detection. To evaluate our proposed method, we develop a new labelled fisheye image dataset that contains two categories. The network model training takes around 3 hours and achieves 100% precious by the test set.",
keywords = "Fisheye image, Image distortion, Orientated Box, YOLO detector, Engineering",
author = "Zhen Chen and Anthimos Georgiadis",
year = "2019",
month = nov,
doi = "10.1109/ICRAE48301.2019.9043800",
language = "English",
isbn = "978-1-7281-4741-3",
series = "International Conference on Robotics and Automation Engineering, ICRAE ",
publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",
pages = "125--129",
booktitle = "2019 4th International Conference on Robotics and Automation Engineering (ICRAE 2019)",
address = "United States",
note = "4th International Conference on Robotics and Automation Engineering, ICRAE 2019, ICRAE2019 ; Conference date: 22-11-2019 Through 24-11-2019",
url = "https://www.ieee-ras.org/component/rseventspro/event/1695-icrae-2019-international-conference-on-robotics-and-automation-engineering",

}

RIS

TY - CHAP

T1 - Learning Rotation Sensitive Neural Network for Deformed Objects' Detection in Fisheye Images

AU - Chen, Zhen

AU - Georgiadis, Anthimos

N1 - Conference code: 4

PY - 2019/11

Y1 - 2019/11

N2 - Object detection plays a significant role in an intelligent system equipped with a fisheye camera. The fisheye image captures a wide field-of-view but deforms in the radial direction. The deformation changes the relative angle between the edge of objects and the image. Therefore, a horizontal bounding box cannot perform an accurate description of an object's location and dimension in advanced neural network training. In this paper, we build a rotation sensitive neural network targeting to realize one-stage regression on the fisheye image detection. The oriented bounding box is applied in the object's description and detection. To evaluate our proposed method, we develop a new labelled fisheye image dataset that contains two categories. The network model training takes around 3 hours and achieves 100% precious by the test set.

AB - Object detection plays a significant role in an intelligent system equipped with a fisheye camera. The fisheye image captures a wide field-of-view but deforms in the radial direction. The deformation changes the relative angle between the edge of objects and the image. Therefore, a horizontal bounding box cannot perform an accurate description of an object's location and dimension in advanced neural network training. In this paper, we build a rotation sensitive neural network targeting to realize one-stage regression on the fisheye image detection. The oriented bounding box is applied in the object's description and detection. To evaluate our proposed method, we develop a new labelled fisheye image dataset that contains two categories. The network model training takes around 3 hours and achieves 100% precious by the test set.

KW - Fisheye image

KW - Image distortion

KW - Orientated Box

KW - YOLO detector

KW - Engineering

UR - http://www.scopus.com/inward/record.url?scp=85083237395&partnerID=8YFLogxK

U2 - 10.1109/ICRAE48301.2019.9043800

DO - 10.1109/ICRAE48301.2019.9043800

M3 - Article in conference proceedings

AN - SCOPUS:85083237395

SN - 978-1-7281-4741-3

T3 - International Conference on Robotics and Automation Engineering, ICRAE

SP - 125

EP - 129

BT - 2019 4th International Conference on Robotics and Automation Engineering (ICRAE 2019)

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

CY - Piscataway

T2 - 4th International Conference on Robotics and Automation Engineering, ICRAE 2019

Y2 - 22 November 2019 through 24 November 2019

ER -