Permute-MAML: exploring industrial surface defect detection algorithms for few-shot learning

Abstract

Computer vision has developed rapidly in recent years, invigorating the area of industrial surface defect detection while also providing it with modern perception capabilities. Few-shot learning has emerged as a result of sample size limitations, with MAML framework being the most widely used few-shot learning framework over the past few years that learns concepts from sampled classification tasks, which is considered to have the key advantage of aligning training and testing objectives. Industrial surface defects typically have fewer samples for training, so we propose MAML-based framework: Permute-MAML, which mainly consists of improved MAML framework and neural network model. In this paper, we concentrate on improving MAML framework with respect to its stability and explore a simple procedure: few-shot learning of its evaluation metrics over the whole classification model. The experimental results demonstrate that the proposed framework significantly enhances the stability of MAML framework and achieves comparatively high accuracy in industrial surface defect detection.