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Steel strip defect detection method and device

A defect detection and steel strip technology, applied in the field of deep learning, can solve the problems of high labor cost, slow speed and low efficiency, and achieve the effect of low labor cost, high detection efficiency and high detection accuracy.

Inactive Publication Date: 2020-12-11
深兰智能科技(上海)有限公司
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  • Abstract
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  • Application Information

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Problems solved by technology

[0003] At present, the detection of steel strip defects is mostly done through manual visual observation, which is slow, inefficient, and high in labor costs. However, the machine-assisted method has frequent false alarms and is difficult to apply to fast-rolling steel coils. problems with detection

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  • Steel strip defect detection method and device
  • Steel strip defect detection method and device
  • Steel strip defect detection method and device

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Embodiment Construction

[0021] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0022] Such as figure 1 As shown, the steel strip defect detection method of the embodiment of the present invention comprises the following steps:

[0023] S1. Obtain a sample data set, wherein the sample data set includes a plurality of sample product images with steel strip defects and a plurality of sample product images without steel strip defects.

[0024] In one embodiment of the present invention, a large number of sample products can be photographed ...

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Abstract

The invention provides a steel strip defect detection method and device, and the method comprises the following steps: obtaining a sample data set which comprises a plurality of sample product imageswith steel strip defects and a plurality of sample product images without steel strip defects; training a neural network through the sample data set to obtain a steel belt defect detection model, andadopting a QuantitNoise-added product quantization algorithm during training; acquiring an image of a to-be-detected product; and inputting the to-be-detected product image into the steel belt defectdetection model to judge whether a steel belt defect exists or not. The method can be suitable for defect detection of the rapidly rolled steel coil strip, and is high in detection efficiency, low inlabor cost and high in detection accuracy.

Description

technical field [0001] The present invention relates to the technical field of deep learning, in particular to a steel strip defect detection method, a steel strip defect detection device, a computer device, a non-transitory computer readable storage medium and a computer program product. Background technique [0002] Surface quality is an important indicator of steel strip quality. With the continuous development of science and technology, the requirements for steel strip surface quality are getting higher and higher. After the steel strip is produced, there may be some defects, such as defects, dislocations, etc., so it is necessary to carry out defect detection before it is put into the market or further processed. [0003] At present, the detection of steel strip defects is mostly done through manual visual observation, which is slow, inefficient, and high in labor costs. However, the machine-assisted method has frequent false alarms and is difficult to apply to fast-rol...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06N3/08G06N3/04
CPCG06T7/0004G06N3/08G06T2207/20081G06N3/045
Inventor 陈海波段艺霖
Owner 深兰智能科技(上海)有限公司
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