Sagger flatness detection device based on binocular structured light and detection method thereof
A detection device and flatness technology, applied in the field of binocular structured light-based sagger flatness detection device, can solve the problems of manual inspection of sagger flatness, high electrode calcination temperature, raw material pollution, etc., to improve detection efficiency and Reliability, high measurement accuracy, and strong environmental adaptability
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Embodiment 1
[0034]The detection object of the saggar flatness detection device based on binocular structured light in this implementation is the saggar, and the structure of the saggar is as followsfigure 1 As shown, the inner bottom of the saggar has four round slight protrusions.
[0035]Referencefigure 2 The sagger flatness detection device based on binocular structured light of this embodiment includes binocular structured light camera 1, three-dimensional point cloud reconstruction module 2, fitting plane module 3, saggar flatness detection module 4, binocular structured light The camera 1 is connected to the 3D point cloud reconstruction module 2, the 3D point cloud reconstruction module 2 is connected to the fitting plane module 3, and the fitting plane module 3 is connected to the saggar flatness detection module 4.
[0036]The binocular structured light camera 1 is used to project the infrared coded structured light onto the sagger to be detected, and collect the binocular sagger coded image...
Embodiment 2
[0053]The difference between this embodiment and embodiment 1 is only: in the saggar flatness detection device based on binocular structured light in this embodiment, the saggar flatness detection module 4 also includes a deep learning network flatness detection module, and deep learning network flatness The degree detection module is used to map the projection distance information collection into a grayscale image, and use the deep learning network to train the grayscale image to further judge the flatness of the sagger.
[0054]ReferenceFigure 4The method for detecting the flatness of the saggar based on binocular structured light of this embodiment further includes step S7: using a deep learning network classification method to further judge the flatness of the saggar, specifically, the flatness of step S6 is judged to be qualified The projection distance information set of the sagger is mapped to data in the range of 0-255, and a grayscale image representing surface fluctuations is...
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