Hot-rolled strip steel surface defect detection method based on generative adversarial network

A technology for hot-rolled steel strip and defect detection, applied in biological neural network models, artificial life, biological models, etc., can solve problems such as low fitting degree and few data samples, achieve data sample enhancement, avoid gradient descent, good recognition effect

Active Publication Date: 2019-08-06
NORTHEASTERN UNIV
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Problems solved by technology

[0005] The purpose of the present invention is to propose a method for detecting surface defects of hot-rolled strip steel based on generative confrontation network. Applyi

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  • Hot-rolled strip steel surface defect detection method based on generative adversarial network
  • Hot-rolled strip steel surface defect detection method based on generative adversarial network
  • Hot-rolled strip steel surface defect detection method based on generative adversarial network

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Embodiment Construction

[0030] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments.

[0031] Such as figure 1 Shown as the realization process of the present invention is as follows:

[0032] Step 1: Collect defect images of hot-rolled strip steel at the industrial site, and do preliminary image preprocessing.

[0033] Step 2: Build and optimize the generative confrontation network model, input the processed real strip surface defect images and condition labels into the model, and observe the output generated images.

[0034] Step 3, the generated image is mixed with the collected hot-rolled strip surface defect image, and the image is processed as a hot-rolled strip surface defect sample set.

[0035] Step 4: Use the particle swarm optimization algorithm to train the improved GAN, extract the discriminant model as a classifier, adjust the parameters, and effectively identify the hot-rolled strip defect sample image data and b...

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Abstract

The invention relates to a hot-rolled strip steel surface defect detection method based on a generative adversarial network, which comprises the following specific steps: (1) extracting an industrialfield hot-rolled strip steel surface defect image, and carrying out image preprocessing; and (2) constructing a generator model and a discriminator model of the generative adversarial network GAN, namely adding a condition label vector c into the input of a generator for outputting a classification image; introducing pixel loss Lp into generator training to improve the quality of the generated image; arranging a discriminator branch and a multi-classification branch in the discriminator, so that a multi-classification function is realized, and the classification precision is improved; (3) optimizing the constructed generative adversarial network parameters by using a PSO (Particle Swarm Optimization); and (4) combining the generated image and the real image into a hot rolled strip steel surface defect sample set. According to the method, the problem of insufficient sample data can be solved, the defect image feature extraction speed and accuracy are improved, and a new effective methodis provided for hot-rolled strip steel surface defect detection.

Description

technical field [0001] The invention belongs to the technical field of computer vision detection, and in particular relates to a method for detecting surface defects of hot-rolled strip steel based on a generative confrontation network. [0002] technical background [0003] The surface defects of strip steel seriously affect the appearance, fatigue resistance, corrosion resistance and wear resistance of steel products, affect the subsequent use of steel products, and cause immeasurable industrial losses. Therefore, defect detection of strip steel products is a very important step in industrial production. The detection method of strip surface defects has been developed from manual to machine detection, which has improved the speed and recognition accuracy. Currently the most common strip defect detection method is to use different means to extract and process defect features, and then use a classifier to classify the defects. In 2002, T Ojala et al. introduced a classifica...

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Application Information

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IPC IPC(8): G06T7/00G06N3/04G06N3/00
CPCG06T7/0004G06N3/006G06N3/045
Inventor 徐林田歌
Owner NORTHEASTERN UNIV
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