| CPC G06T 7/0004 (2013.01) [G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30124 (2013.01)] | 21 Claims |
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1. A method of identifying defects in a fabric, the method comprising:
obtaining an image of the fabric on a loom;
extracting feature points indicative of an intersection between a weft and a warp within the image to generate an input image, wherein the input image comprises a mapping of the feature points within the image, and wherein the input image has an area defined by a plurality of the feature points;
processing the input image with a first machine learning model;
detecting one or more defects within the input image using the first machine learning model;
providing, by the first machine learning model, an indication of a defect in the input image; and
providing a second machine leaning model, wherein the second machine learning model is configured to perform a further verification step of the one or more defects to confirm or deny the presence of the one or more defects of the detection from the first machine learning model, wherein the first machine learning model comprises a first neural network and the second machine learning model comprises a second neural network that is different from the first neural network, wherein the first neural network and the second neural network have different parameters,
wherein the identifying defects in the fabric is performed in real-time for a defect inspection that is to perform in-situ defect detection to pause faulty production of the fabric for correction, wherein the correction involves, when the further verification step of the one or more defects confirms the presence of the one or more defects, stopping the loom to correct a cause of the one or more defects.
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