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A method of locating the 3D centers of retroreflectors based on deep learning

BinBin Zhang (State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China)
Fumin Zhang (State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China)
Xinghua Qu (State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University, Tianjin, China)

Industrial Robot

ISSN: 0143-991x

Article publication date: 19 January 2021

Issue publication date: 3 August 2021

85

Abstract

Purpose

Laser-based measurement techniques offer various advantages over conventional measurement techniques, such as no-destructive, no-contact, fast and long measuring distance. In cooperative laser ranging systems, it’s crucial to extract center coordinates of retroreflectors to accomplish automatic measurement. To solve this problem, this paper aims to propose a novel method.

Design/methodology/approach

We propose a method using Mask RCNN (Region Convolutional Neural Network), with ResNet101 (Residual Network 101) and FPN (Feature Pyramid Network) as the backbone, to localize retroreflectors, realizing automatic recognition in different backgrounds. Compared with two other deep learning algorithms, experiments show that the recognition rate of Mask RCNN is better especially for small-scale targets. Based on this, an ellipse detection algorithm is introduced to obtain the ellipses of retroreflectors from recognized target areas. The center coordinates of retroreflectors in the camera coordinate system are obtained by using a mathematics method.

Findings

To verify the accuracy of this method, an experiment was carried out: the distance between two retroreflectors with a known distance of 1,000.109 mm was measured, with 2.596 mm root-mean-squar error, meeting the requirements of the coarse location of retroreflectors.

Research limitations/implications

The research limitations/implications are as follows: (i) As the data set only has 200 pictures, although we have used some data augmentation methods such as rotating, mirroring and cropping, there is still room for improvement in the generalization ability of detection. (ii) The ellipse detection algorithm needs to work in relatively dark conditions, as the retroreflector is made of stainless steel, which easily reflects light.

Originality/value

The originality/value of the article lies in being able to obtain center coordinates of multiple retroreflectors automatically even in a cluttered background; being able to recognize retroreflectors with different sizes, especially for small targets; meeting the recognition requirement of multiple targets in a large field of view and obtaining 3 D centers of targets by monocular model-based vision.

Keywords

Acknowledgements

This study was supported by the National Natural Science Foundation of China (grant numbers: 51775379 and 51675380) and by National Key R&D program of China (grant number: 2018YFF0212702 and 2018YFB2003501).

Citation

Zhang, B., Zhang, F. and Qu, X. (2021), "A method of locating the 3D centers of retroreflectors based on deep learning", Industrial Robot, Vol. 48 No. 3, pp. 352-358. https://doi.org/10.1108/IR-09-2020-0186

Publisher

:

Emerald Publishing Limited

Copyright © 2020, Emerald Publishing Limited

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