基于改进Faster R-CNN的电力检修工程智慧监测  

Intelligent Monitoring of Power Maintenance Engineering Based on Improved Faster R-CNN

作　　者：唐小煜[1,2,3] 罗杰豪 黄声勇 TANG Xiaoyu;LUO Jiehao;HUANG Shengyong(School of Electronic and Information Engineering,South China Normal University,Foshan 528000,China;Xingzhi College,School of Data Science and Engineering,South China Normal University,Shanwei 516600,China;Shanwei Power Supply Bureau of Guangdong Grid Co.,Ltd.,Shanwei 516600,China)

机构地区：[1]华南师范大学电子与信息工程学院,佛山528000 [2]华南师范大学数据科学与工程学院行知书院,汕尾516600 [3]广东电网有限责任公司汕尾供电局,汕尾516600

出　　处：《华南师范大学学报（自然科学版）》2024年第4期60-67,共8页Journal of South China Normal University(Natural Science Edition)

基　　金：国家自然科学基金项目(61371176);广东大学生科技创新培育专项资金(“攀登计划”专项资金)项目(pdjh2022a0131)。

摘　　要：提出一种改进Faster R-CNN的目标检测模型用于智能化电力检修工程,构建了电力检修设备的数据集,并对其进行了数据清洗和数据增强。为了提高模型对被物体在遮挡以及复杂背景情况的识别能力,在模型中嵌入了精心设计的ESAM注意力模块,优化了模型的特征提取能力。ESAM注意力模块通过多尺度处理和跨空间学习方法,在保持计算效率的同时,能够更好地捕捉和利用图像特征中的重要信息,从而提高模型的整体性能。设计了多组实验并可视化模型效果,实验结果进一步验证了模型的有效性。该研究对于提高电力检修工作的效率和质量,保障电力系统的安全运行,从而支撑“双碳”目标的实现,具有一定的理论意义和实际价值。An improved Faster R-CNN object detection model is proposed for intelligent power maintenance engineering.A dataset of power maintenance equipment was constructed,along with data cleaning and data augmentation.An attention module designed to optimize feature extraction has been incorporated into the model to enhance recognition of occluded and complex background objects.Through multi-scale processing and cross-space learning methods,the model is able to capture and utilize critical information in the image features while maintaining computational efficiency,thereby improving overall performance.Several experiments were designed and the model effects visualized.The experimental results further validate the effectiveness of the model.This research holds theoretical significance and practical value for enhancing the efficiency and quality of power maintenance work,ensuring the safe operation of power systems,and supporting the realization of the“Dual Carbon”goals.

关 键 词：电力检修 “双碳”政策 数据增强 多尺度处理 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]