Insulator visual non-conformity detection in overhead power distribution lines using deep learning. (September 2019)

• Record Type: Journal Article
• Authors: Prates, Ricardo M.; Cruz, Ricardo; Marotta, André P.; Ramos, Rodrigo P.; Simas Filho, Eduardo F.; Cardoso, Jaime S.
• Is Part Of: Computers & electrical engineering. Volume 78(2019)
• Page Start: 343
• Page End: 355
• Publication Date: 2019-09
• Subjects: Overhead Distribution Power Lines (ODPLs) -- Insulators -- Automated inspection -- Data augmentation -- Convolutional Neural Networks (CNNs) -- Multi-task learning (MTL); Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2019.08.001
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Overhead Power Distribution Lines (OPDLs) correspond to a large percentage of the medium-voltage electrical systems. In these networks, visual inspection activities are usually performed without resorting to automated systems, requiring a significant investment of time and human resources. We present a methodology to identify the defect and type of insulators using Convolutional Neural Networks (CNNs). More than 2500 photographs were collected both from inside a studio and from a realistic OPDL. A classification model is proposed to automatically recognize the insulators conformity. This model is able to learn from indoors photographs by augmenting these images with realistic details such as top ties and real-world backgrounds. Furthermore, Multi-Task Learning (MTL) was used to improve performance of defect detection by also predicting the insulator class. The proposed methodology is able to achieve an accuracy of 92% for material classification and 85% for defect detection, with F1-score of 0.75, surpassing available solutions.