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Bathroom ceramic surface defect detection method and device based on deep learning

A defect detection and ceramic surface technology, applied in the field of visual inspection, can solve the problems of high requirements for professional knowledge and the robustness of the algorithm needs to be further improved, and achieve the effect of accurate identification and positioning

Active Publication Date: 2020-02-21
FOSHAN UNIVERSITY
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Problems solved by technology

Therefore, this type of method has high requirements for the relevant professional knowledge of developers, and at the same time, when the recognition task changes, the robustness of the algorithm needs to be further improved

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  • Bathroom ceramic surface defect detection method and device based on deep learning
  • Bathroom ceramic surface defect detection method and device based on deep learning
  • Bathroom ceramic surface defect detection method and device based on deep learning

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Embodiment Construction

[0047] The concept, specific structure and technical effects of the present disclosure will be clearly and completely described below in conjunction with the embodiments and drawings, so as to fully understand the purpose, scheme and effect of the present disclosure. It should be noted that, in the case of no conflict, the embodiments in the present application and the features in the embodiments can be combined with each other.

[0048] According to the first aspect of the application, reference figure 1 , a method for detecting surface defects of sanitary ceramics based on deep learning provided in the embodiment of the present application, comprising the following steps:

[0049] Step S100, acquiring an image data set, which is a collection of surface images of sanitary ceramics to be inspected.

[0050] In an optional embodiment, an industrial camera is used to collect images on the surface of the sanitary ceramics under the illumination of a coaxial light source, so as t...

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Abstract

The invention relates to the technical field of visual inspection, in particular to a bathroom ceramic surface defect detection method and a device based on deep learning, and the method comprises thesteps: firstly obtaining an image data set which is a set of surface images of to-be-detected bathroom ceramic; marking the defect type and the minimum defect bounding rectangle of the surface imageto generate an image feature set; dividing the image feature set into a training set, a verification set and a test set, and generating a candidate box according to the minimum defect bounding rectangle of the training set; inputting the candidate boxes into a Faster R-CNN neural network for training, and generating a surface defect detection model; and finally, inputting the test set into the detection model, and detecting the defect type of the to-be-detected bathroom ceramic. The bathroom ceramic surface defect detection method can be used for accurately identifying and positioning bathroomceramic surface defects.

Description

technical field [0001] The invention relates to the technical field of visual inspection, in particular to a method and device for detecting surface defects of sanitary ceramics based on deep learning. Background technique [0002] In the actual production line of the factory, due to the existence of uncontrollable factors such as product transportation, unstable manufacturing process, and human interference, the product inevitably has certain defects. Among them, the impact of surface defects on product quality is particularly prominent, such as scratches, cracks, etc. Such defects not only affect the appearance of the product but even cause certain functions of the product to not be realized normally. Therefore, surface defect detection is the key to improving production efficiency and yield. For the toilet workpiece fired by spraying glaze, the surface of the workpiece is smooth and easy to reflect light, and the contrast of the defect part is not obvious, so it is diffi...

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08G06K9/62
CPCG06N3/08G06V20/00G06N3/045G06F18/23213G06F18/214Y02P90/30
Inventor 王志锋周壮壮卢清华
Owner FOSHAN UNIVERSITY
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