書誌事項
- タイトル別名
-
- Feature Space Interpretation of Deep Neural Network (DNN) for Visual Inspection Using Artificial Inspection Images
抄録
<p>In this study, we propose a method to clarify the application limits of DNN (Deep Neural Network) based visual inspection systems. A process for determining inspection results is a black box because DNN automatically extracts features. However, visual inspection requires judgment based on specifications. The problem is that the basis for the decision is unclear. To address this problem, we interpret the feature space of DNN using known features. Firstly, it generates data with explicit knowledge characteristics (for example, defect length, area, shading depth etc.) that can be arbitrarily modified. Secondary, the generated data are input to trained DNN models and observed the coordinate changes caused by their explicit knowledge characteristics in the feature space. Preliminary experiments on the MNIST dataset (public dataset) confirm that the DNN feature space is able to represent quantitative feature variation. Experiments using actual inspection images also confirmed the effectiveness of the proposed method.</p>
収録刊行物
-
- 電気学会論文誌Ｃ（電子・情報・システム部門誌）
-
電気学会論文誌Ｃ（電子・情報・システム部門誌） 143 (11), 1073-1082, 2023-11-01
一般社団法人 電気学会
- Tweet
キーワード
詳細情報 詳細情報について
-
- CRID
- 1390579454814919808
-
- ISSN
- 13488155
- 03854221
-
- 本文言語コード
- ja
-
- データソース種別
-
- JaLC
- Crossref
-
- 抄録ライセンスフラグ
- 使用不可