Surface defect detection method and device

Abstract

The invention relates to the technical field of image processing, and provides a surface defect detection method and device. The method comprises the following steps: firstly, extracting each bottom-layer feature of a to-be-detected surface defect image to obtain each feature image pyramid, determining each feature image corresponding to each feature image pyramid according to a center peripheral difference mechanism, normalizing each feature image, and adding the feature images of the same type to obtain each feature saliency map; fusing the feature saliency maps by taking the energy proportion of each feature saliency map as a weight to obtain a synthetic saliency map, then extracting, fusing and sampling high-level features of the surface defect image to obtain a high-level saliency map; and finally, taking the energy proportion of the synthetic saliency map and the energy proportion of the high-level saliency map as a weight, fusing the synthetic saliency map and the high-level saliency map to obtain a total saliency map, and determining defect types and defect positions of the surface defect image according to the total saliency map. According to the invention, the recognition precision of surface defect detection is improved.

Description

technical field [0001] The embodiments of the present application relate to the technical field of image processing, and in particular to a surface defect detection method and device. Background technique [0002] With the rapid development of economic construction and infrastructure construction, steel plates, one of the main products of the steel industry, are widely used in various industries, and people have higher and higher requirements for the quality of steel plates. However, due to the influence of equipment, production technology and other factors, various defects will inevitably occur on the surface of the steel plate during the production process. These defects not only affect the appearance of the steel plate, but also affect its mechanical properties to a certain extent. The related research of defect detection is of great significance to improve the quality of steel plate. With the gradual increase of output and the increase of production speed, the tradition...

AI Technical Summary

Problems solved by technology

However, due to the influence of equipment, production technology and other factors, various defects will inevitably occur on the surface of the steel plate during the production process. These defects not only affect the appearance of the steel plate, but also affect its mechanical properties to a certain extent. Research on defect detection is of great significance for improving the quality of steel plates

With the gradual increase of output and the increase of production speed, the traditional manual visual sampling inspection has been difficult to meet the actual needs of the current production of enterprises. Improving the efficiency of steel plate surface defect detection to meet more needs of production is of great importance to iron and steel enterprises. important value

[0003] At present, the detection method based on machine vision is widely used in the detection of steel plate surface defects. The traditional machine vision detection system usually extracts image features first and then uses a classifier for classification detection. Small size, large amount of data information and less useful information, which affects the detection effect to a certain extent; machine vision detection method based on deep learning requires a large number of defect images as training samples, and the number of defective steel plates in the production process After all, it is a small number. It is difficult to obtain a large number of defect image samples. Too few samples will lead to poor detection results.

[0004] Therefore, the current surface defect detection scheme cannot better improve the recognition accuracy of surface defect detection, and needs to be improved

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