DocumentCode
3075321
Title
Defect Recognition of X-Ray Steel Rope Cord Conveyer Belt Image Based on BP Neural Network
Author
Wen, Wang ; Chang-yun, Miao ; Ji, Wang ; Xian-guo, Li
Author_Institution
Sch. of Inf. & Commun. Eng., Tianjin Polytech. Univ., Tianjin, China
fYear
2011
fDate
16-17 July 2011
Firstpage
168
Lastpage
171
Abstract
BP neural network is used to recognize X-ray steel rope cord conveyer belt image with defect in this paper. Firstly, the model of three layers BP neural network is established, and it is made up of 240 input nodes, 20 hidden layer nodes, and 1 output node. Then, the BP neural network is trained and tested in MATLAB. The results show that X-ray steel rope cord conveyer belt image with defect can be identified by the neural network.
Keywords
X-ray imaging; backpropagation; belts; conveyors; mechanical engineering computing; neural nets; object recognition; pulleys; ropes; BP neural network; MATLAB; x-ray steel rope cord conveyer belt image defect recognition; Belts; Biological neural networks; Feature extraction; Image recognition; Neurons; Steel; Training; BP neural network; MATLAB; X-ray steel rope cord conveyer belt image with defect; recognition;
fLanguage
English
Publisher
ieee
Conference_Titel
Computer Science and Society (ISCCS), 2011 International Symposium on
Conference_Location
Kota Kinabalu
Print_ISBN
978-1-4577-0644-8
Type
conf
DOI
10.1109/ISCCS.2011.53
Filename
6004411
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