An Incremental Meta Defect Detection System for Printed Circuit Boards

Defect detection is essential in production lines to guarantee the quality of products. However, detecting tiny defects is difficult. Besides, as the variety of products increases, the variety of defects also increases. Models take much time to retrain. In this paper, we propose an 'Incremental Meta Defect Detection (IMDD) System,' which utilizes incremental meta-learning to detect tiny defects. We decompose the model into feature pyramids and use feature alignment to improve the sensitivity of minor defects. Incremental learning utilizes knowledge distillation but this affects the learning of new categories, so the model is quickly adapted to new categories. We further combine incremental learning with meta-learning to increase the generality of the model. In experiments, the proposed model is 1.14 times more accurate than previous techniques. Therefore, the proposed system can enhance the ability to identify minor defects and quickly adapt to new defect types.