.png){kind=icon}
.png){kind=icon}
Return
Class-Incremental Learning for Surface Defect Detection
.png){kind=icon}
PRE
AI
DOI：10.1007/978-981-95-3393-0_46.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Surface defect detection is crucial for quality control and production optimization in industrial processes. However, traditional surface defect detection models have focused only on the closed static detection scenario. This paper aims to investigate the surface defect detection task to the more practical incremental detection. First, to cope with the performance requirements of industrial quality inspection, we propose an improved Deformable DETR. Specifically, a joint positional encoding method is introduced to enhance the model's global perception. Then, the improved Deformable DETR is combined with an knowledge distillation to propose a class-incremental surface defect detection method. Experimental results in the surface defect detection scenario show that the proposed method achieves competitive performance and demonstrate the effectiveness of the proposed method in surface defect detection.
Keywords:
Class incremental learning
Surface defect detection
Knowledge distillation
Journal
I
IF:
0
Papers:
37
Citations:
0