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
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;
Conference_Titel :
Computer Science and Society (ISCCS), 2011 International Symposium on
Conference_Location :
Kota Kinabalu
Print_ISBN :
978-1-4577-0644-8
DOI :
10.1109/ISCCS.2011.53
