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15 November 2017 An online ID identification system for liquefied-gas cylinder plant
Jin He, Zhenwen Ding, Lei Han, Hao Zhang
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Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 106052V (2017) https://doi.org/10.1117/12.2294485
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China
Abstract
An automatic ID identification system for gas cylinders’ online production was developed based on the production conditions and requirements of the Technical Committee for Standardization of Gas Cylinders. A cylinder ID image acquisition system was designed to improve the image contrast of ID regions on gas cylinders against the background. Then the ID digits region was located by the CNN template matching algorithm. Following that, an adaptive threshold method based on the analysis of local average grey value and standard deviation was proposed to overcome defects of non-uniform background in the segmentation results. To improve the single digit identification accuracy, two BP neural networks were trained respectively for the identification of all digits and the easily confusable digits. If the single digit was classified as one of confusable digits by the former BP neural network, it was further tested by the later one, and the later result was taken as the final identification result of this single digit. At last, the majority voting was adopted to decide the final identification result for the 6-digit cylinder ID. The developed system was installed on a production line of a liquefied-petroleum-gas cylinder plant and worked in parallel with the existing weighing step on the line. Through the field test, the correct identification rate for single ID digit was 94.73%, and none of the tested 2000 cylinder ID was misclassified through the majority voting.
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Jin He, Zhenwen Ding, Lei Han, and Hao Zhang "An online ID identification system for liquefied-gas cylinder plant", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 106052V (15 November 2017); https://doi.org/10.1117/12.2294485
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KEYWORDS
Image segmentation
System identification
Neural networks
Image acquisition
Binary data
Light sources and illumination
Cameras
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