Strip Surface Defect Classification Method Based on the Combination of Statistical Features and Image Features

Abstract

The strip surface defect classification method based on the combination of statistical features and image features includes the steps of: collecting a set of marked training sample sets; S2, extracting the corner points of each defect sample image and describing each corner point; The corner point description of the sample image performs unsupervised learning clustering in the K-dimensional space; the M-dimensional image feature vector and the N-dimensional statistical feature vector of the defect sample are combined to form the M+N-dimensional feature vector of the defect sample; using adaptive promotion The tree training method performs supervised learning training on the M+N dimensional feature vector, and the training comes from the learning classifier B, and outputs the classification result of the defect. The invention distinguishes different defects and improves the correct rate of classification of steel strip surface defects; when surface defects are detected online in real time, the self-learning classifier is used to automatically and accurately classify the detected defects, and the classification rules do not need to be manually relied on manually input, but obtained through supervised learning using machine learning techniques.

Description

technical field [0001] The invention belongs to the field of steel strip surface defect detection systems in the metallurgical industry, in particular to the field of surface defect classification, and specifically relates to a strip steel surface defect classification method based on the combination of statistical features and image features. Background technique [0002] Strip surface defects are an important factor affecting the surface quality of cold-rolled strip, directly affecting the appearance and performance of the final product. The surface inspection system uses camera sensors to scan the surface of the steel strip to obtain two-dimensional images of the surface of the steel strip, and uses machine vision technology to detect and classify surface defects. At present, the accuracy of defect detection based on various advanced algorithms has reached 98% %, which basically meets the production requirements, but the correct rate of defect classification is always uns...

AI Technical Summary

Problems solved by technology

[0003] The technical problem to be solved by the present invention is to provide a strip surface defect classification method based on the combination of statistical features and image features for the above-mentioned deficiencies in the existing strip surface defect classification, so as to improve the accuracy of the strip surface quality detection system. The correct rate of classification of steel surface defects; when surface defects are detected online in real time, the self-learning classifier is used to automatically and accurately classify the detected defects, and the classification rules do not need to rely on manual input

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