A Deep Learning-based General Defect Detection Framework for Automated Optical Inspection

Artificial intelligence (AI) is applied in automated optical inspection (AOI) to help inspect defects and reduce the false discovery rate of AOI in manufacturing industries. In current studies, the training data of AI models are sufficient, and the source data are from the specific production line. However, defect samples are insufficient, and the data source is variant. The current models need more generalization to all machines and take a long training time to build a new model for other appliances. This paper proposes a Deep Learning-based General Defect Detection Framework (DL-GDD) solves the insufficient data issue and the generalization issue of models. We implement a color preprocessing module, a data augmentation module, a data generation module, and four classification models to detect defects and generalize the utilization of DLG-DD. In experiments, we evaluate DLG-DD based on the NEU-CLS and AIdea datasets. The accuracy of DLG-DD is 90%, and the false omission rate and false discovery rate are less than 1%. DLG-DD is a general framework that tackles insufficient data and decreases the false discovery rate of AOI.