Conveyor belt deviation detection method based on dual flow network

Among the traditional belt edge detection methods, the contact detection technology has high cost and the non-contact detection technology has low precision. With the development of artificial intelligence technology, although the method based on convolutional neural network can effectively improve...

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Bibliographic Details
Published inMéitàn kēxué jìshù Vol. 51; no. S2; pp. 259 - 267
Main Authors Zhifang YANG, Liya ZHANG, Bonan HAO, Yuan LIU, Qing ZHAO
Format Journal Article
LanguageChinese
Published Editorial Department of Coal Science and Technology 01.12.2023
Subjects
belt deviation
edge detection
encoder-decoder
image segmentation
neural network
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Summary:Among the traditional belt edge detection methods, the contact detection technology has high cost and the non-contact detection technology has low precision. With the development of artificial intelligence technology, although the method based on convolutional neural network can effectively improve the detection accuracy, but limited by the local operation characteristics of the convolutional operation itself, there are still problems such as insufficient perception of long-distance and global information, it is difficult to improve the accuracy of the belt edge detection. In order to solve the above problems, ① by combining the traditional convolutional neural network's ability to extract local features and the Transformer structure's ability to perceive global and long-distance information, a dual-flow transformer network (DFTNet) which integrates global and local information is proposed. The edge detection network model can better improve the belt edge detection accuracy and suppress the interference of be
ISSN:0253-2336
DOI:10.13199/j.cnki.cst.2023-0215