Machine Vision Automatic Inspection Technology of Optical Fiber Winding Based on Deep Learning

doi:10.12068/j.issn.1005-3026.2021.01.011

Abstract

Abstract: Existing optical fiber winding inspection methods have poor generalization ability and environmental adaptability， and cannot be applied to industrial production. A machine vision method based on deep learning was proposed to classify the winding images during the winding process to solve the optical fiber winding problem. By considering the effect of the force between the optical fibers when the optical fiber was winding， the optical fiber winding model was established， and the speed expression of the arranging mechanism was proposed when the optical fiber was winding. The camera was used to collect a large number of optical fiber winding pictures to form a data set， and a neural network model was built and trained to classify the winding situation. Experimental verification showed that the accuracy of this method for gap state recognition is over 94.67%， and the accuracy of overlapped line recognition is 100%. The inspection speed is higher than the actual production winding speed. It is a favorable method that can be combined with the control system to replace manual winding and realize automatic precision winding.

Key words: optical fiber winding; deep learning; machine vision; defect detection; automatic detection

CLC Number:

TP399

LIU Yu， WEI Xi-lai， WANG Shuai， DAI Li. Machine Vision Automatic Inspection Technology of Optical Fiber Winding Based on Deep Learning[J]. Journal of Northeastern University(Natural Science), 2021, 42(1): 68-74.

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