Ingot scratch detection system and method based on depth learning and image processing technology
A technology of deep learning and image processing, applied in image data processing, image enhancement, image analysis, etc., can solve the problems of low detection accuracy, low classification accuracy, and low efficiency of wire spindle defects, and achieve manpower saving and high classification accuracy , the effect of high detection accuracy
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[0077] In this embodiment, a wire ingot scratch detection system based on deep learning and image processing technology, such as figure 1 As shown, it includes a tray 2 loaded with silk spindles, a label is set on the silk spindles, a conveyor belt 1 that transmits the trays, a dark box 3 is set on the transmission belt, and a sorting unit 5 is set on the transmission belt behind the dark box. An image acquisition unit 4 for acquiring label images and silk spindle images is provided, and the image acquisition unit sends the acquisition information to the processing unit for scratch target detection. The processing unit identifies and reads the label information from the label graphics, extracts the target detection area from the silk spindle image, inputs the target detection area image into the trained deep learning CNN network, extracts the scratch target detection frame image, and assigns the scratch target Scratch score of the detection frame image, input ...
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