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Object surface pattern screening method based on artificial neural network

An artificial neural network and screening method technology, applied in the field of object surface pattern screening, can solve the problems of uncontrollable yield rate of structural objects, prone to misjudgment, poor efficiency, etc.

Pending Publication Date: 2021-04-20
SHENXUN COMP KUNSHAN +1
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

However, the efficiency of manually detecting whether a structural object has defects is low, and misjudgment is very easy to occur, which will cause the yield rate of the structural object to be uncontrollable

Method used

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  • Object surface pattern screening method based on artificial neural network
  • Object surface pattern screening method based on artificial neural network
  • Object surface pattern screening method based on artificial neural network

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Embodiment Construction

[0039] The object surface screening method based on artificial neural network is suitable for an artificial neural network system. Here, the artificial neural network system can be implemented on a processor.

[0040] In some embodiments, the processor can perform deep learning of multiple sub-neural network systems (that is, artificial neural networks that have not been trained) on the same or different multiple object images under different training conditions to respectively establish multiple sub-neural network systems for use. A prediction model (that is, a trained artificial neural network) is used to identify the surface type of the object, and these sub-neural network systems are connected in series to form an artificial neural network system. Here, these object images may be images of surfaces of the same object at the same relative position. In other words, when the surface of the object has any surface types, the corresponding image positions of the object image of...

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Abstract

The invention discloses an object surface pattern screening method based on an artificial neural network. The method comprises the following steps of: receiving at least one object image; using a first prediction model to identify the surface pattern of each object image so as to classify the object images into a first normal group and a first abnormal group; and performing surface pattern recognition of each output image in the first normal group by using a second prediction model so as to classify the output images into a second normal group and a second abnormal group. According to the object surface pattern screening method based on the artificial neural network, surface pattern recognition is continuously performed on the object images through the plurality of neural networks which are connected in series and have different training conditions so as to precisely and rapidly classify the object images, objects corresponding to the object images are efficiently screened based on the classification result of the object images, and then a low over-discharge rate is obtained.

Description

【Technical field】 [0001] The invention relates to an artificial neural network training system, in particular to an artificial neural network-based object surface type screening method. 【Background technique】 [0002] Various safety protection measures are composed of many small structural objects, such as seat belts. If the strength of these small structural objects is insufficient, the protective effect of safety protection measures can be questioned. [0003] Due to various reasons during the manufacturing process of these structural objects, such as collisions, process errors, mold defects, etc., tiny defects on the surface, such as slots, cracks, bumps, and textures, etc., may occur on the surface. These tiny flaws are not easy to detect. One of the existing defect inspection methods is to manually observe the structural object to be inspected with naked eyes or touch with both hands to determine whether the structural object has defects, such as pits, scratches, colo...

Claims

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Application Information

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IPC IPC(8): G01N21/956
Inventor 蔡昆佑杨博宇
Owner SHENXUN COMP KUNSHAN
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