架空地線上を自走するカメラ画像を用いた色を手がかりにした架空地線の異常検出手法の開発

カテゴリ: 論文誌(論文単位)

グループ名: 【B】電力・エネルギー部門

発行日: 2020/04/01

タイトル(英語): Abnormality Detection of Ground Wire Based on Color Histogram using Images Taken from Monitoring Machine

著者名: 石野　隆一（（一財）電力中央研究所），篠原　靖志（（一財）電力中央研究所）

著者名(英語): Ryuichi Ishino (CRIEPI), Yashusi Sinohara (CRIEPI)

キーワード: 架空地線，点検，異常検出，画像処理，機械学習　　ground wire，inspection，abnormality detection，image processing，machine learning

要約(英語): Arc marks and cut wires on an ground wire are mainly checked through by a helicopter. When the helicopter cannot be used, a machine that incorporate a video camera is used. The machine attached wheels runs on the ground wire and takes a video of ground wire. After recoding videos, a worker check whether or not, there is an arc mark and cut wire in the video. There are few faults in the video. The task is very bored for the worker, therefore, it is required to reduce the amount of the video that the worker has to check. We have developed a new method that extracts images that could include those faults and discards other images. The method detects an arc mark, cut wire and corrosion product that appears on the surface of the ground wire due to inner corrosion, based on color feature histogram. The features are learned by one of machine learning method, which is called Support Kernel Machine (SKM). To verify the method, 100 images including arc marks and 186 images including corrosion products are used. 89 arc marks images are detected, 169 images that corrosion products appear are detected. Through the verification, the effectiveness of the proposed method was presented.

本誌: 電気学会論文誌B（電力・エネルギー部門誌） Vol.140 No.4 （2020） 特集：最近の電線・ケーブル技術

本誌掲載ページ: 292-298 p

原稿種別: 論文／日本語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejpes/140/4/140_292/_article/-char/ja/