湖北民族大学学报（自然科学版）2025，Vol.43Issue(3)：351-356,6.DOI:10.13501/j.cnki.42-1908/n.2025.09.013
小样本目标检测模型在电力行业的应用与发展趋势
Application and Development Trends of Few-shot Object Detection Model in the Power Industry
摘要
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.关键词
小样本/目标检测/深度学习/电力行业/检测精度Key words
few-shot/object detection/deep learning/power industry/detection accuracy分类
信息技术与安全科学引用本文复制引用
高林,郜世佳,焦思航,刘华国..小样本目标检测模型在电力行业的应用与发展趋势[J].湖北民族大学学报（自然科学版）,2025,43(3):351-356,6.基金项目
国家自然科学基金项目(12464004,61562025) （12464004,61562025）
湖北民族大学校内科学研究项目(XN2317) （XN2317）
广西高校中青年教师科研基础能力提升项目(2024KY0816). （2024KY0816）
