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《Journal of Electronic Measurement and Instrumentation》 2017-11
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Research on image edge detection algorithm for pipeline corrosion visual measurement

Li Zhonghu;Zhang Lin;Yan Junhong;Department of Information Engineering College,Inner Mongolia University of Science and Technology;  
In order to detect the edge of pipeline inner corroded image,the classic edge detection method is analyzed. And it is found that the detection precision is not high and the anti-noise performance is poor. On this basis,an image edge detection algorithm based on BP neural network is researched. To build the BP neural network,standard image is made as input data,and the edge image of standard image detected by traditional edge detection operator is made as output data. And a large amount of data is used for training. Finally,the experimental result of the edge detection of the corroded image inside the pipeline detected with the BP neural network method is given,and it is compared with the result of traditional edge detection algorithm. The results show that the proposed algorithm can improve the detection precision and anti-noise ability significantly,and it is a kind of algorithm with extensive adaptability.
【Fund】: 国家自然科学基金(61362023)资助项目
【CateGory Index】: TP183;TP391.41
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