Meta-learning-based few-shot image detection of defects in substation equipment
Defect image detection is an important technical tool for substation equipment operation and maintenance. However,due to the scarcity of defect samples,the traditional deep learning model based on massive data training faces the challenge of few-shot defect detection in practical applications. Therefore,this article introduces the idea of meta-learning and proposes a deep learning model for few-shot defect image detection of substation equipment. The core of the model is the optimization of front-end network weights and model fine-tuning for few-shot testing tasks. The former enables the model to quickly adapt to new tasks through a task generation strategy based on semantic information,while the latter fine-tune the model through a network optimization method based on meta-learning. Therefore,the model can obtain excellent performance on new tasks. The experimental results show that the improved method can enhance the model's overall detection accuracy by 7.33％ and the detection accuracy of the new categories by 11.48％,which significantly improves the detection performance on few-shot defects and defects of new categories.
万方数据
维普
