Industrial magnetic ring defect detection based on image processing and small sample deep learning

Within the domain of ferrite ring production, the vast majority of available data consists of repetitive, defect-free samples, while data containing defects are considerably rare. To tackle this issue, a deep learning-based defect detection strategy tailored for small sample scenarios has been introduced. The approach begins with the precise extraction of defective areas using image masking technology, followed by the processing of these defect images through regional enhancement transformation techniques to create new instances of defects. These newly generated defect images are employed to build an offline defect sample library. During the model training phase, defective samples are randomly selected from this library and merged with the foreground of normal samples to generate new defect images for training. The deep learning network trained using this methodology demonstrates enhanced capability in distinguishing between defective and defect-free data, with experimental results showing an increase of 3.87% in the Area Under the Curve (AUC) metric. This approach not only addresses the challenge of scarce defect data but also significantly improves the performance of defect detection in small sample environments.