A weak supervision machine vision detection method based on artificial defect simulation

Abstract

During a practical detection process, insufficient defect data, unbalanced defect types and the high cost of defect labeling can present problems. Therefore, it often takes considerable time and labor to collect actual samples to improve the accuracy of defect classification and recognition. In this paper, we propose a weak supervision machine vision detection method based on artificial defect simulation. First, four typical mobile phone screen defects – scratches, floaters, light stains and severe stains – are simulated by the proposed synthesis algorithms, and an artificial defect database is created. Next, the artificial dataset is applied to a deep learning recognition algorithm, and an initial model is trained. Then, the collected actual defects are augmented due to the insufficient training quantity. The augmented actual defects are then applied as the training data, and the initial model is retrained by fine tuning. Finally, the well-retrained model is used for defect recognition. The experimental results demonstrate that satisfactory performance is achieved with the proposed detection method.