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

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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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. Applying GAN to the detection of surface defects of hot-rolled strip steel can solve the problems of few data samples and low fitting degree, and improve the recognition accuracy

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