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列車前方画像を用いた木まくらぎ劣化度判定AIシステムの開発

列車前方画像を用いた木まくらぎ劣化度判定AIシステムの開発

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カテゴリ: 論文誌(論文単位)

グループ名: 【D】産業応用部門

発行日: 2024/03/01

タイトル(英語): Development of AI System for Inspecting Wooden Sleepers Deterioration Using Train Forward View Images from Train Cab

著者名: 前田 梨帆((公財)鉄道総合技術研究所),長峯 望((公財)鉄道総合技術研究所),合田 航((公財)鉄道総合技術研究所),坪川 洋友((公財)鉄道総合技術研究所),加藤 爽((公財)鉄道総合技術研究所)

著者名(英語): Riho Maeda (Railway Technical Research Institute), Nozomi Nagamine (Railway Technical Research Institute), Wataru Goda (Railway Technical Research Institute), Yosuke Tsubokawa (Railway Technical Research Institute), So Kato (Railway Technical Research Institute)

キーワード: 木まくらぎ検査,列車前方画像,画像処理,Deep Learning  wooden sleeper inspection,forward view image from train cab,image processing,deep learning

要約(英語): In railway track maintenance, the inspection of wooden sleepers is crucial for the safety of train operations. However, the inspection of wooden sleepers is basically performed through manual visual inspection by workers, which is time-consuming and labor-intensive. Therefore, we developed a wooden sleeper inspection system that uses images of the front of a train obtained by a camcorder. The proposed system uses deep learning to judge the deterioration of the images and ranks the degree of deterioration. This paper outlines the developed system, evaluates its judgment accuracy, and compares the results with judgments made by multiple train maintenance engineers. Accordingly, the accuracy of the proposed method in accurately judging the degree of deterioration reached 86.3% to 94.1% when compared with the experts' results, indicating that the developed deterioration judgment model has comparable performance to that of the train maintenance engineers in terms of judgment accuracy and distribution of answers.

本誌: 電気学会論文誌D(産業応用部門誌) Vol.144 No.3 (2024) 特集:J-RAIL 2022

本誌掲載ページ: 79-86 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/ieejias/144/3/144_79/_article/-char/ja/

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