Fast Visual Identification and Location Algorithm forIndustrial Sorting Robots Based on Deep Learning

WU Xiru; HUANG Guoming; SUN Lining

doi:10.13973/j.cnki.robot.2016.0711

Volume 38 Issue 6

Nov. 2016

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ROBOT > 2016 > 38(6): 711-719. > DOI: 10.13973/j.cnki.robot.2016.0711 CSTR: 32165.14.robot.2016.0711

WU Xiru, HUANG Guoming, SUN Lining. Fast Visual Identification and Location Algorithm forIndustrial Sorting Robots Based on Deep Learning[J]. ROBOT, 2016, 38(6): 711-719. DOI: 10.13973/j.cnki.robot.2016.0711

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Fast Visual Identification and Location Algorithm forIndustrial Sorting Robots Based on Deep Learning

WU Xiru^1,2,3,
HUANG Guoming^1,2, ,,
SUN Lining³

1. College of Electronic Engineering and Automation, Guilin University of Electronic Technology, Guilin 541004, China;
2. Guangxi Key Laboratory of Automatic Detection, Guilin University of Electronic Technology, Guilin 541004, China;
3. College of Electrical and Mechanical Engineering, Soochow University, Suzhou 215006, China

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Received Date: May 10, 2016
Revised Date: November 02, 2016
Available Online: October 26, 2022
Published Date: November 19, 2016

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Abstract

Abstract

To overcome the problems of slow recognition, low accuracy and inaccurate positioning for industrial sorting robots, a fast visual identification and location algorithm based on deep convolutional neural network (CNN) is proposed. Firstly, the target image information is obtained by an industrial precision camera, and the target image is located and segmented through graying, filtering, Otsu binarization and boundary detection of the images. Secondly, the target object is identified by using a trained CNN, and its position coordinate and class are obtained. Thus, target sorting by industrial robots is realized. Finally, the Chinese chess with complex lines are taken in test experiments to verify the identification and location algorithm. Experimental results show that the locating error is lower than 0.8 mm, the fastest recognition speed can reach 0.049 seconds per target, and the identification accuracy can be kept over 98% in the experimental environment. So, the proposed algorithm has good accuracy and stability.
- deep learning,
- convolutional neural network,
- visual identification,
- location algorithm,
- industrial sorting robot

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