Automated System for Defect Identification and Character Recognition using IR Images of SS-Plates

Defects on the surface of stainless steel(SS) plates are one of the most important factors affecting the quality of SS plates. Problems of manual defect inspections are lack of accuracy and high time consumption, where early and accurate defect detection is a significant phase of quality control. It is indeed in need to distinguish such abnormalities through computer automated classification systems, which would have a persistent vision of identifying and classifying the above mentioned problem with self-trained classification routine. In this paper, develop a sophisticated routine for defect identification and character recognition on SS plates by considering the multiple features of IR images. The proposed method integrates four steps: (1) defect candidate is detected using a Multi-Scale LoG Weighting; (2) features descriptive of defect shape and texture are extracted; (3) defect objects are classified using a classifier based on SVM-RFE model and (4) the character recognition of SS plate is done using pattern correlation. The output of the anticipated routine is assessed by the metrics: accuracy, sensitivity & specificity. The automated defect identification and classifying routine is compared with ANN, Adaboost and Random Forest (RF) classification methods where the classification result of the anticipated routine outperformed the performance of the previous classification methods.