객체 수준 데이터 증강을 활용한 딥러닝 기반의 강판 표면 결함 검출

Abstract

The detection of surface defects in the steel manufacturing process is a very important issue in terms of quality assurance and facility management. Recently, many studies have been conducted to improve the performance of detection by applying deep learning models. However, it is still difficult to detect surface defects because of class imbalance problem. In this study, we propose a deep learning model using object-level data augmentation method to improve the detection accuracy of steel sheet surface defect images with class imbalance. We demonstrated the usefulness and applicability of the proposed method by conducting various experiments using Severstal steel defect detection dataset. We applied various deep learning model architectures to the proposed method and proved that its performance was improved compared to the model using existing image-level data augmentation method. Further, the experimental results showed that the proposed method especially improves the defect detection performance of minor classes.