Application and Development Trends of Few-shot Object Detection Model in the Power Industry
GAO Lin
GAO Shijia
JIAO Sihang
LIU Huaguo
Abstract：To address the issue of limited generalization in traditional object detection models due to scarce complex defect samples and diverse fault types in the power industry,the application status of few-shot object detection(FSOD)models across the five stages of power industry,namely,power generation,transmission,transformation,distribution,and consumption was systematically studied,and corresponding development trends were analyzed.It was found that FSOD models exhibited excellent performance in defect detection,fault detection,equipment monitoring,and safety inspection through strategies such as meta-learning,transfer learning,data augmentation,and metric learning.However,challenges were encountered in model structure and perception under complex backgrounds,generalization and data fusion,and sample dependency and system engineering,necessitating further optimization.FSOD models could have development in model structure improvements,multimodal data fusion,and domain knowledge integration with system deployment optimization.This study provided an important reference for the deepen application of FSOD models in the power industry.
Keywords：few-shotobject detectiondeep learningpower industrydetection accuracy
Publication Date：2025-09-20
Online Publishing Date：2025-09-24(First online date of this platform, not the publication date of the document)
Pages：6( 351-356 )