Defect detection and recognition based on ADABOOT-SVM integrated model

Abstract

As the core component of printing machinery, the surface finish and geometric accuracy of printing drum will have an important impact on the quality of printed matter. However, the use of acid ink, alcohol and other chemical raw materials corrode the drum, leading to local collapse or spots. How to effectively identify the types of drum defects has become an important issue. To solve this problem, a defect detection and recognition framework based on adaboot-SVM ensemble learning model is proposed. The framework is composed of two parts: feature extraction and classifier design. The first part is feature extraction from directional gradient histogram (HOG). In the second part, we construct an ensemble of different SVM classifiers to identify defects. The validity of the proposed model is verified by nine different defects. The results show that the integrated model of adaboot SVM is helpful to improve the recognition accuracy of defects.