A speed and precision balanced steel product surface defect detection method

Abstract

The invention discloses a speed and precision balanced steel product surface defect detection method, and belongs to the technical field of steel surface defect detection, and the method comprises the following steps: S1, obtaining a hot rolled strip steel surface defect typical image sample from a database, and preprocessing the sample; s2, on the basis of a Center Net target detection model, designing a hot-rolled strip steel surface defect target detection model comprising skip connection and a pyramid feature fusion module, and selecting a backbone network as a feature extractor of the target detection model; s3, initializing parameters of a backbone network structure part in the target detection model by adopting parameters trained on an ImageNet data set, and training the target detection model by using a training sample; and S4, testing a test sample by using the trained target detection model, and outputting a detection result. The invention is high in accuracy of detecting the target position of the surface defect of the hot-rolled strip steel, high in detection speed and capable of being effectively applied to field real-time detection of the surface defect of the hot-rolled strip steel.

Description

technical field [0001] The invention relates to the technical field of steel surface defect detection, in particular to a method for detecting surface defects of steel products with a balance between speed and precision. Background technique [0002] In the production process of hot-rolled strip steel, due to various physical and chemical factors and the complexity of the hot-rolling process, the surface of the strip steel is prone to various types of scale, scratches, pockmarks, inclusions, plaques, cracks, etc. Such defects seriously affect the performance and appearance of the product. The production and flow of defective products to the market will bring huge economic and commercial reputation losses to product manufacturers. Therefore, real-time and correct detection of surface defects of hot-rolled strip is very important for strip production and quality control. [0003] At present, the inspection of steel surface defects is mainly divided into manual observation an...

AI Technical Summary

Problems solved by technology

The experimental results indeed prove that this method can improve the defect detection ability of the detection model. However, it does not design a special solution for the surface defect images of hot-rolled strips with different characteristics, but integrates almost all levels of feature maps uniformly.

In addition, the selected network backbone has more training parameters, and does not consider the detection speed requirements in the actual industrial production environment.

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