ひび割れ検出性能評価の問題点及び対応策

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: 2023/12/01

タイトル(英語): Issues in Evaluation of Crack Detection Methods and a Proposed Solution

著者名: ラシキア　城治（中京大学工学部情報工学科）

著者名(英語): George Lashkia (Department of Information Engineering, Chukyo University)

キーワード: 深層学習，セマンティックセグメンテーション，ひび割れ検出，指標　　deep learning，semantic segmentation，crack detection，metrics

要約(英語): Automatic crack detection is an essential task for the effective maintenance of roads and structures. In recent years, deep learning has been widely applied to crack detection, and many models have been proposed. However, there isn't a standardized metric for the evaluation of crack detection methods nor reliable comparisons. This paper addresses this issue by examining the evaluation metrics suitable for cracks, selecting useful ones, and suggesting improvements. Additionally, a crack detection evaluation software was proposed and tested on the CRACK500 dataset, comparing FPHBN and DAUNet crack detection methods. The results confirmed the efficacy of the proposed metrics and the proposed software in helping researchers evaluate and compare different methods efficiently and effectively.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.143 No.12 （2023） 特集：電気・電子・情報関係学会東海支部連合大会

本誌掲載ページ: 1196-1202 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/143/12/143_1196/_article/-char/ja/