Textile fabric surface defect detection method based on artificial intelligence and Gaussian mixture model
A technology of Gaussian mixture model and defect detection, which is applied in neural learning methods, character and pattern recognition, biological neural network models, etc., to achieve the effects of strong practicability, improved accuracy and training speed, and improved generalization ability
Inactive Publication Date: 2022-05-24
南通海恒纺织设备有限公司
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[0004] The invention provides a textile surface defect detection method based on artificial intelligence and a Gaussian mixture model to solve existing problems
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[0041] The following will be combined with the accompanying drawings in the embodiments of the present invention, the technical solution in the embodiments of the present invention will be described clearly and completely, it is clear that the embodiments described are only a part of the embodiment of the present invention, not all embodiments. Based on embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative work, are within the scope of protection of the present invention.
[0042] Embodiments of the present invention based on artificial intelligence and Gaussian mixing model of textile surface defect detection method, the method comprising:
[0043] S1, collect textile images and obtain the gray scale of textile images, and divide the gray map to obtain multiple block images.
[0044] Specifically, the RGB image of the textile surface is obtained by the camera, and the grayscale image is grayscaled to obtai...
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The invention relates to a textile fabric surface defect detection method based on artificial intelligence and a Gaussian mixture model, and the method comprises the steps: obtaining a gray-scale map of a textile fabric image, and segmenting the gray-scale map into a plurality of block images; obtaining a plurality of Gaussian mixture sub-models according to pixel point position distribution and gray value change characteristics of the block image; the method comprises the following steps: acquiring a defect ratio of Gaussian mixture sub-models which may have defects according to the Gaussian mixture sub-models, acquiring a training image set according to the defect ratio, training a constructed defect detection network model according to the training image set, and detecting a to-be-detected textile image by using the trained defect detection network model. According to the method, the to-be-detected textile fabric image is detected through the trained defect detection network model, the detection efficiency is improved, meanwhile, the defect detection accuracy is improved, the practicability is high, and the method is worthy of popularization.
Description
Technical field [0001] The present invention relates to the field of textile defect detection technology, specifically to a textile surface defect detection method based on artificial intelligence and Gaussian mixing model. Background [0002] With the continuous development of machine vision and artificial intelligence, it is gradually applied to the field of textile surface defect detection. In the prior art, for the detection of textile surface defects, patent number CN101063660, the patent name is a textile defect detection method and its apparatus discloses the use of wavelet transform to decompose the image wavelets, obtain high-frequency information and approximate components in different directions, and use adaptive threshold processing to locate textile defects. [0003] However, although the textile surface texture information can be obtained through high-frequency information in different directions, the textile defect is also high-frequency information compared to the...
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Login to View More IPC IPC(8): G06T7/00G06T7/10G06V10/82G06V10/762G06N3/04G06N3/08G01N21/88
CPCG06T7/0004G06T7/10G06N3/08G01N21/8851G01N2021/8887G06T2207/20081G06T2207/20084G06T2207/20021G06T2207/20032G06T2207/30124G06N3/045G06F18/23Y02P90/30
Inventor 邓存芳
Owner 南通海恒纺织设备有限公司