Title :
Recognition of bolt and nut using artificial neural network
Author :
Johan, Teuku Muhammad ; Prabuwono, Anton Satria
Author_Institution :
Center for Artificial Intell. Technol. (CAIT), Univ. Kebangsaan Malaysia, Bangi, Malaysia
Abstract :
This paper focuses on the recognition system of bolt and nut in real time for application in various industries, particularly the automotive industry. The objective of this study is to develop the image processing algorithm to get the normalized cropping images which would be suitable inputs for the learning process using Backpropagation Neural Network. Testing is done using a real-time visual recognition system. The Matlab software version 7.6 is used to integrate all algorithms, whereas the stepper motor differentiates the final result of bolt and nut in separate places. The result shows that the system can detect moving object accurately on the belt conveyor at a speed of 9 cm/sec. with an accuracy 92%.
Keywords :
automobile industry; automotive components; backpropagation; conveyors; fasteners; image motion analysis; neural nets; object detection; object recognition; production engineering computing; Matlab software; artificial neural network; automotive industry; backpropagation neural network; belt conveyor; bolt recognition; image processing algorithm; learning process; moving object detection; normalized cropping image; nut recognition; real-time visual recognition system; stepper motor; Artificial neural networks; Fasteners; Humans; Image processing; MATLAB; Training; Pattern recognition; artificial neural network; bolt and nut;
Conference_Titel :
Pattern Analysis and Intelligent Robotics (ICPAIR), 2011 International Conference on
Conference_Location :
Putrajaya
Print_ISBN :
978-1-61284-407-7
DOI :
10.1109/ICPAIR.2011.5976889