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Data Augmentation Method Using Diffusion Models for Tomato Leaf Discrimination Problem
http://hdl.handle.net/2237/0002012759
http://hdl.handle.net/2237/00020127593dcf341d-88f3-4ffa-8174-140acceca9e0
| 名前 / ファイル | ライセンス | アクション |
|---|---|---|
template.pdf (21.5 MB)
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| アイテムタイプ | itemtype_ver1(1) | |||||||||
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| 公開日 | 2025-05-16 | |||||||||
| タイトル | ||||||||||
| タイトル | Data Augmentation Method Using Diffusion Models for Tomato Leaf Discrimination Problem | |||||||||
| 言語 | en | |||||||||
| 著者 |
Oirase, Masaya
× Oirase, Masaya
× Kita, Eisuke
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| アクセス権 | ||||||||||
| アクセス権 | open access | |||||||||
| アクセス権URI | http://purl.org/coar/access_right/c_abf2 | |||||||||
| 権利 | ||||||||||
| 権利情報 | This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s12626-025-00178-6 | |||||||||
| 言語 | en | |||||||||
| 内容記述 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | In the field of infrastructure maintenance and management, the practical application of deep learning-based anomaly detection models utilizing images or videos as input is advancing to enhance the efficiency of anomaly detection. However, training a supervised image classification model requires substantial amount of data, which is often unavailable. This lack of sufficient training data frequently limits model performance practical application. Data augmentation is often performed as a method to improve accuracy with limited data. In recent years, technology for image generation based on diffusion models has rapidly advanced, and it has been shown that increasing the amount of training data through data generation using diffusion models can improve model performance. However, only general label generation is typically performed, posing challenges in generating rare anomaly data that exist in the real-world scenarios. This study proposes a new data augmentation method combining geometric pattern mask images and diffusion models to address this gap. By capturing the features of the original image in the unmasked areas and generating the masked regions, new images can be generated while preserving the features of the original labels, facilitating the generation of rare anomaly data. The experimental data uses a dataset of tomato leaf lesion images. The change in model performance when training image classification models with limited data using the proposed method is confirmed experimentally. Experimental results showed up to a 19.50% improvement in accuracy with the proposed data augmentation method. Furthermore, additional experiments demonstrated even greater accuracy improvements when combined with other data augmentation techniques. Notably, as this method does not require text prompts for generation, it holds potential for utility across diverse datasets. | |||||||||
| 言語 | en | |||||||||
| 出版者 | ||||||||||
| 出版者 | Springer | |||||||||
| 言語 | en | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプresource | http://purl.org/coar/resource_type/c_6501 | |||||||||
| タイプ | journal article | |||||||||
| 出版タイプ | ||||||||||
| 出版タイプ | AM | |||||||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||||||
| 関連情報 | ||||||||||
| 関連タイプ | isVersionOf | |||||||||
| 識別子タイプ | DOI | |||||||||
| 関連識別子 | https://doi.org/10.1007/s12626-025-00178-6 | |||||||||
| 収録物識別子 | ||||||||||
| 収録物識別子タイプ | EISSN | |||||||||
| 収録物識別子 | 1867-3236 | |||||||||
| 書誌情報 |
en : The Review of Socionetwork Strategies 巻 19, p. 69-82, 発行日 2025-04 |
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| ファイル公開日 | ||||||||||
| 日付 | 2026-04-01 | |||||||||
| 日付タイプ | Available | |||||||||
