Edge algorithm optimization of Faster R-CNN algorithm for fault identification of transmission lines
-
摘要: 为提升输电线路缺陷识别效果,研究了一种利用边缘算法优化Faster R-CNN算法的输电线路缺陷识别方法。通过无人机采集输电线路图像,采用极值中值滤波算法降噪,输入Faster R-CNN模型,提取缺陷特征,利用RPN网络确定目标候选区域;利用边缘算法优化Faster R-CNN算法以确定像素点梯度幅值,并抑制非极大值;训练模型,完成输电线路缺陷识别。测试结果显示,该算法能够提升各主要缺陷类别的识别准确率,准确率达85%以上。Abstract: To improve the effectiveness of transmission line defect recognition, this paper studied a transmission line defect recognition method that utilized edge algorithms to optimize the Faster R-CNN algorithm. Transmission line images through drones were collected. Extreme median filtering algorithm to reduce noise was used. Faster R-CNN model was inputted and defect features were extracted. RPN network to determine target candidate regions was used. Faster R-CNN algorithm using edge algorithm was optimized to determine pixel gradient amplitude and suppress non maximum values. The model was trained to complete the identification of transmission line defects. The test results showed that the algorithm studied can improve the recognition accuracy of various major defect categories, with an accuracy rate of over 85%.
-
-
[1] 周湛,张志坤,赵振刚,等.基于光纤传感的输电线路悬垂绝缘子风偏角监测研究[J].电子测量与仪器学报,2020,34(3):81-87. [2] 陈大兵,魏寒来,胡轶宁,等.碳纤维复合芯导线X射线图像标准化增强与缺陷检测方法[J].数据采集与处理,2020,35(4):739-744. [3] 方志丹,林伟胜,范晟,等.基于层级识别模型的输电线路杆塔小金具缺陷识别方法[J].电力信息与通信技术,2020,18(9):16-24. [4] 曾勇斌,王星华,彭显刚,等.输电线路缺陷风险建模及其预测方法研究[J].电力系统保护与控制,2020,48(10):91-98. [5] 黄广龙.基于无人机图像识别技术的水利工程输电线路缺陷检测方法[J].水利科技与经济,2022,28(8):137-141. [6] NI H X,WANG M Z,ZHAO L Y.An improved Faster R-CNN for defect recognition of key components of transmission line[J].Mathematical Biosciences and Engineering:MBE,2021,18(4):4679-4695.
[7] 王红星,陈玉权,张欣,等.基于离线高斯模型的输电线路无人机巡检缺陷智能识别方法研究[J].电测与仪表,2022,59(3):92-99. [8] 钟嘉俊,贺德强,苗剑,等.基于改进Faster R-CNN的地铁车辆焊缝缺陷检测[J].铁道科学与工程学报,2020,17(4):996-1003. [9] 梁玉珠,梅雅欣,杨毅,等.一种基于边缘计算的传感云低耦合方法[J].计算机研究与发展,2020,57(3):639-648. [10] 马媛媛,刘周斌,汪自翔.边缘计算场景下的异构终端安全接入技术研究[J].计算机工程与应用,2020,56(17):115-120. [11] HU K,WANG B J,SHEN Y,et al.Defect identification method for poplar veneer based on progressive growing generated adversarial network and MASK R-CNN model[J].BioResources,2020,15(2):3041-3052.
[12] 丁旭阳, 谢盈, 张小松. 基于边缘计算的进化多目标优化图像隐写算法[J]. 计算机研究与发展, 2020, 57(11):12-22. [13] 赵海涛,朱银阳,丁仪,等.车联网中基于移动边缘计算的内容感知分类卸载算法研究[J].电子与信息学报,2020,42(1):20-27. [14] 翟瑞聪,林俊省,郑桦.基于图像识别的输电线路设备缺陷识别应用系统设计[J].电子设计工程,2022,30(6):161-164,169. [15] 熊小萍,许爽,蒙登越,等.基于Faster R-CNN的输电线路缺陷识别模型研究[J].自动化与仪器仪表,2020(3):1-6.
计量
- 文章访问数: 13
- HTML全文浏览量: 0
- PDF下载量: 7
下载:
