24 October 2017 Identification of conveyor belt injury based on image texture SVM classification method
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Proceedings Volume 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications; 104620D (2017) https://doi.org/10.1117/12.2282029
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China
In the field of steel wire core conveyor belt detection, the X-ray detection method has been widely used for its accuracy and reliability. But as a result of the monotonous, baldness and plenty of X-ray image, it is necessary to apply the computer image processing techniques to identification wire injury accurately and automatically. Then the injury can be located in the actual conveyor belt to maintenance and repair. And the joint is an important part of the conveyor belt, for it is easy to recognize in the practical application, so as the benchmark to joint reference point positioning fault is a good choice. Therefore, the accurate identification of joint is very important to conveyor belt injury locational. At present there are some algorithms applying the method of detecting domain gray level or horizontal gradient change frequency in the identification of joint. These algorithms can be accurate to single image of joint without considering the practical complex and changeable X-ray image for the different thickness of the conveyor belt outer rubber. For single image characteristics of the proposed algorithm is easy to failure in practical application. A new robust algorithm is necessary to solve this problem. And SVM(Support Vector Machine) is a novel method of machine learning evolving from Statistics. SVM presents many own advantages in solving machine learning problems such as small samples, nonlinearity and high dimension. In this paper, the image texture SVM classification method construct feature vectors through the extraction of image gray level co-occurrence matrix texture information. classified feature vectors using the SVM classification method to determine whether the image contains joint and provide the joint location information. The mentioned texture information include gray-level co-occurrence matrix energy, contrast and entropy. And the gray level co-occurrence matrix reflects the image direction, adjacent interval and the change in value of integrated information. SVM classification method is applied to locate the joints number and position on the real conveyor belt. And by means of image binarization, skeleton and such as pretreatments, this paper use the method of template matching for the identification wire fracture. Finally the method locate injury on the real conveyor belt according to the fracture position and joint position of the pixel distance. The results show that the image texture SVM classification method can effectively combine the method of template matching for the identification of conveyor belt injury.
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Chen Yang, Chen Yang, Yajun Song, Yajun Song, Jinbao Yang, Jinbao Yang, Yachao Liu, Yachao Liu, } "Identification of conveyor belt injury based on image texture SVM classification method", Proc. SPIE 10462, AOPC 2017: Optical Sensing and Imaging Technology and Applications, 104620D (24 October 2017); doi: 10.1117/12.2282029; https://doi.org/10.1117/12.2282029

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