ZHAO Yanfei, YANG Yanli, WANG Lijua. Surface fault location of conveyor belt based on saliency and deep convolution neural network[J]. Journal of Mine Automation, 2016, 42(12): 72-77. DOI: 10.13272/j.issn.1671-251x.2016.12.016
Citation:
ZHAO Yanfei, YANG Yanli, WANG Lijua. Surface fault location of conveyor belt based on saliency and deep convolution neural network[J]. Journal of Mine Automation, 2016, 42(12): 72-77. DOI: 10.13272/j.issn.1671-251x.2016.12.016
ZHAO Yanfei, YANG Yanli, WANG Lijua. Surface fault location of conveyor belt based on saliency and deep convolution neural network[J]. Journal of Mine Automation, 2016, 42(12): 72-77. DOI: 10.13272/j.issn.1671-251x.2016.12.016
Citation:
ZHAO Yanfei, YANG Yanli, WANG Lijua. Surface fault location of conveyor belt based on saliency and deep convolution neural network[J]. Journal of Mine Automation, 2016, 42(12): 72-77. DOI: 10.13272/j.issn.1671-251x.2016.12.016
A surface fault location of conveyor belt based on saliency and deep convolution neural network was proposed. The method imprints figures on the edge of upper and lower surfaces of conveyor belt, and uses image processing technology to detect the number in belt image, so as to indirectly locate surface fault of the conveyor belt. Firstly, the acquired image of the conveyor belt is preprocessed by Gaussian filtering and gray-scale linear transformation to improve image quality and enhance contrast between the background and the target. Then, visual saliency treatment is conducted to the preprocessed image according to spectral residual theory, and a visual saliency map containing numeric regions is obtained. Finally, saliency map is classified by using the convolution neural network to distinguish digital region from non-digital region. The experimental results show that the method can detect number of conveyor belt image and realize surface fault location of conveyor belt.
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