| Title |
A study on Learning Methods for Power Transmission Facilities based on Deep Learning using Multi Segmentation and Tagging |
| Authors |
정남준(Nam-Joon Jung) ; 황명하(Myeong-Ha Hwang) ; 이동혁(Dong-Hyuk Lee) ; 송운경(Un-Kyung Song) |
| DOI |
https://doi.org/10.5370/KIEE.2022.71.2.436 |
| Keywords |
Deep Learning; Convolutional Neural Network; Power Transmission Facility Diagnosis; Object Segmentation and Tagging |
| Abstract |
Recently, with the development of deep learning technology, failure analysis and failure diagnosis research using image analysis of objects have been actively conducted. In particular, research on algorithm and system development for diagnosing facilities using drone photographed images is being applied to the industrial field. The results are reaching the level of commercialization. In the electricity field, drone images have been used in the field of power facility diagnosis since two to three years ago. There are not many abnormal learning data to determine whether transmission facilities are abnormal, so full-scale use in the actual field is limited. Therefore, this study proposes a method of securing more learning data by utilizing images of limited failure data. In addition, the obtained data is used for learning to present deep learning methods and research results for developing a more accurate transmission facility diagnosis system. As a result of this study, it was confirmed that the average precision was improved by about twice from 39.2% to 81.1% by applying the learning model technology. This improved method of artificial intelligence learning technology is expected to prevent power transmission failure in advance, avoid power outage costs caused by failure, and reduce maintenance costs through inspection automation. |