深層学習を用いた孔壁展開画像における亀裂検出

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2024/07/01

タイトル(英語): Crack Detection in Borehole-wall Panoramic Images using Deep Learning

著者名: 和田　直史（北海道科学大学），鈴木　利実（(株)レアックス），立野　直樹（(株)レアックス）

著者名(英語): Naofumi Wada (Hokkaido University of Science), Toshimi Suzuki (RaaX Co., Ltd.), Naoki Tatsuno (RaaX Co., Ltd.)

キーワード: ボアホールカメラ，孔壁展開画像，亀裂検出，深層学習　　borehole camera，borehole-wall panoramic image，crack detection，deep learning

要約(英語): In geological surveys, a borehole camera is used to photograph the vertical cylindrical borehole-wall to investigate underground cracks. Currently, the identification of cracks from borehole-wall images is performed visually by skilled workers, which requires a great deal of time and effort. In this study, we use deep learning to detect sine-curve-like cracks from borehole-wall panoramic images. We designed a two-class classification model that discriminates the presence or absence of cracks using existing network architectures. Furthermore, we introduced a new data augmentation technique called “CyclicShift”, which takes advantage of the unique properties of borehole-wall panoramic images. Through experiments using our own dataset, we showed that both WideResNet and ViT achieve over 98% accuracy under the limited condition of a single crack in one image. Additionally, we confirmed the effectiveness of data augmentation and fine-tuning of pre-trained models. We also demonstrated the potential of using Grad-CAM to locate the positions of cracks.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.144 No.7 （2024） 特集：2023年電子・情報・システム部門大会

本誌掲載ページ: 658-664 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/ieejeiss/144/7/144_658/_article/-char/ja/