基于Mask R-CNN的复合绝缘子过热缺陷检测  

Overheating Defect Detection of Composite Insulator Based on Mask R-CNN

作　　者：高熠 田联房[1] 杜启亮[1] GAO Yi;TIAN Lianfang;DU Qiliang(School of Automation Science and Engineering,South China University of Technology,Guangzhou 510641,China)

机构地区：[1]华南理工大学自动化科学与工程学院,广东广州510640

出　　处：《中国电力》2021年第1期135-141,共7页Electric Power

基　　金：中央高校基本科研业务费专项资金资助项目(2018KZ05);广东省自然资源厅海上风电专项资助项目(x2zd/B4200280)。

摘　　要：针对当前基于复合绝缘子红外图的过热缺陷检测技术中存在的工作量大、智能化程度低,以及传统的图像分割方法在复杂背景下分割不精确且泛化性能差的问题,提出了一种基于实例分割网络Mask RCNN的复合绝缘子过热缺陷检测方法。首先,该方法为提高分割精度,借鉴Cascade R-CNN的思路对Mask RCNN网络进行改进,并在模型训练中使用数据增强、迁移学习等方法提升网络表现。接着,该方法对深度分割网络得到的结果使用传统图像处理的骨架化等方法做进一步优化,使得最终的分割结果只覆盖复合绝缘子芯棒部分。最后,该方法直接读取红外图中自带的温度数据并转换成实际的温度值,根据DL/T664—2016《带电设备红外诊断应用规范》中的相关方法与标准实现对过热缺陷的等级判断。研究结果表明,该文提出的算法对出现严重缺陷及紧急缺陷的复合绝缘子红外图检测准确率较高,都是100%,而无过热缺陷或者一般缺陷的红外图会出现误检现象,总体上在测试集的缺陷检测中取得了93%的准确率。Aiming at the problems of large workload and low intelligence of the current infrared image-based overheating defect detection techniques for composite insulators,and the poor accuracy and poor generalization performance of the traditional image segmentation methods in complex backgrounds,an overheating defect detection method is proposed for composite insulators based on instance segmentation network Mask R-CNN.Firstly,in order to improve the accuracy of segmentation,the Mask R-CNN network is improved according to the idea of Cascade R-CNN,and the data augmentation and transfer learning methods are used for model training to improve the network performance.Secondly,the result obtained by deep segmentation network is further optimized by using traditional image processing methods such as skeletonization,so that the final segmentation result only covers the core rod of the composite insulators.Finally,the temperature data in the infrared image is directly read and converted into the actual temperature value,and the grade of overheating defects is judged according to the relevant methods and criteria provided in DL/T664—2016 Infrared Diagnostic Application Specification for Live Equipment.The results show that the algorithm proposed in this paper has a high detection accuracy of 100%for the infrared images of composite insulators with serious and urgent defects,but has false detection occurrence for the infrared images without overheating defects or with general defects.On the whole,the accuracy rate of 93%is achieved in defect detection of test sets.

关 键 词：图像检测 Mask R-CNN Cascade R-CNN 迁移学习 复合绝缘子 红外图 过热缺陷 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TM216[自动化与计算机技术—控制科学与工程] TP391.41[一般工业技术—材料科学与工程]