Class-incremental printed circuit board defect detection method based on YOLOX
To cope with more practical incremental printed circuit board detection scenarios,by combining the know-ledge distillation with the YOLOX,this paper proposes a class-incremental Printed Circuit Board defect detection meth-od based on YOLOX.The model can detect all learned defect types when only new training data is used.The transfer of knowledge about old defect categories is facilitated by using knowledge distillation for the model's output features and intermediate features,enabling the student model to effectively retain the detection performance of the teacher model on old defect categories.The experimental results show that the method in this paper can significantly alleviate the cata-strophic forgetting problem during the incremental learning process.Under the two-stage incremental scenario,the mod-el has a mean average precision of 88.5%for all defects,a parameter size of 25.3 M,and an inspection speed of 39.8 f/s,which facilitates the deployment of industrial equipment and at the same time,it can satisfy the detection accuracy of printed circuit board(PCB)quality inspection and the inspection speed requirement in incremental detection scenarios.
deep learningprinted circuit boardclass-incrementalincremental learningdefect detectionobject detec-tiondynamic detectionknowledge distillationcatastrophic forgetting