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 -