Product surface defect detection method, device and computer device
A defect detection and product technology, which is applied in the direction of measuring devices, calculations, and optical testing of defects/defects, etc., can solve problems such as large amount of calculation, large space, and model occupation, and achieve the effect of low calculation amount and storage space
Inactive Publication Date: 2019-01-25
SHENZHEN LINTSENSE TECH CO LTD
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However, the deep neural network with better detection effect often has a large number of nodes (neurons) and model parameters, which not only has a large amount of calculation, but also occupies a large part of the space in the actual deployment, which limits its application to storage and computing resources. restricted devices
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[0015] The subject matter described herein will now be discussed with reference to example implementations. It should be understood that the discussion of these implementations is only to enable those skilled in the art to better understand and realize the subject matter described herein, and is not intended to limit the protection scope, applicability or examples set forth in the claims. Changes may be made in the function and arrangement of elements discussed without departing from the scope of the disclosure. Various examples may omit, substitute, or add various procedures or components as needed. For example, the methods described may be performed in an order different from that described, and various steps may be added, omitted, or combined. Additionally, features described with respect to some examples may also be combined in other examples.
[0016] As used herein, the term "comprising" and its variants represent open terms meaning "including but not limited to". The...
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A product surface defect detection method and device, a computer device and a computer-readable medium are disclosed. The method includes obtaining an image to be detected including a surface of the product; detecting the image to be detected by using a first neural network model to determine whether the product surface contained in the image to be detected has defects; Wherein the first neural network model is obtained by compressing the trained second neural network model with a genetic algorithm, the second neural network model is obtained by training with a preset training sample, and theaccuracy of the first neural network model based on the training sample is not lower than a preset accuracy. The method and the device utilize the neural network model after the compression treatmentby the genetic algorithm to carry out defect detection on the surface of the product, have the advantages of low calculation amount and storage space, and can be applied to the equipment with limitedstorage and calculation resources.
Description
technical field [0001] The present application relates to the field of computer application technology, in particular to a product surface defect detection method and device, computer equipment and computer readable media. Background technique [0002] In recent years, with the development of artificial intelligence, the neural network (Neural Network, NN) algorithm has been widely used in the field of product surface defect detection, such as fabric defect detection, electronic component surface defect detection, etc. However, the deep neural network with better detection effect often has a large number of nodes (neurons) and model parameters, which not only has a large amount of calculation, but also occupies a large part of the space in the actual deployment, which limits its application to storage and computing resources. All restricted devices. Contents of the invention [0003] In view of the above problems, embodiments of the present invention provide a product sur...
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IPC IPC(8): G06T7/00G06N3/12G01N21/88
CPCG01N21/8851G01N2021/8887G06N3/126G06T7/0004G06T2207/10004G06T2207/20081G06T2207/20084G06T2207/30108
Inventor 金玲玲饶东升何文玮
Owner SHENZHEN LINTSENSE TECH CO LTD