Enhanced Wheat Head Detection in Images Using Fourier Domain Adaptation and Random Guided Filter: Détection améliorée des têtes de blé dans les images à l’aide de l’adaptation du domaine Fourier et du filtre guidé aléatoire
| Title: | Enhanced Wheat Head Detection in Images Using Fourier Domain Adaptation and Random Guided Filter: Détection améliorée des têtes de blé dans les images à l’aide de l’adaptation du domaine Fourier et du filtre guidé aléatoire |
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| Authors: | Sylvester C. Okafor, Linjing Wei, Solomon Boamah, Le Zhang, Mamadou B. Diallo |
| Source: | Canadian Journal of Remote Sensing, Vol 50, Iss 1 (2024) |
| Publisher Information: | Taylor & Francis Group, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Environmental sciences LCC:Technology |
| Subject Terms: | Environmental sciences, GE1-350, Technology |
| More Details: | Wheat head detection is essential in estimating the important characteristics of wheat. However, detecting wheat heads in images from different domains has been challenging due to variations in domain features and environmental conditions. This research aims to improve the robustness of wheat head detection in wheat images. A combination of Fourier domain adaptation (FDA), adaptive alpha beta gamma correction (AABG) and random guided filter (RGF) preprocessing methods was applied in this study. The authors utilized FDA to reduce variations between different domains by transforming an image into the Fourier domain, aligning its distribution with a randomly selected image of another domain. AABG adjusts image properties based on local statistics of the image patches, and RGF, a technique for edge-aware image filtering, was used as augmentation. An EfficientDet model was trained on the publicly available wheat dataset and the results were analyzed and compared to a baseline model. The FDA + RGF approach achieved an improved mean average precision (mAP) of 0.6534 compared to the baseline mAP of 0.6292. Our study can contribute to advancing wheat head detection techniques in agriculture, addressing factors like variations in wheat head appearance by focusing on improving domain variation through data dependent approaches. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English French |
| ISSN: | 1712-7971 07038992 |
| Relation: | https://doaj.org/toc/1712-7971 |
| DOI: | 10.1080/07038992.2024.2367479 |
| Access URL: | https://doaj.org/article/a5db1ad37b3b42a1b24693b0609bbbe4 |
| Accession Number: | edsdoj.5db1ad37b3b42a1b24693b0609bbbe4 |
| Database: | Directory of Open Access Journals |
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| Items | – Name: Title Label: Title Group: Ti Data: Enhanced Wheat Head Detection in Images Using Fourier Domain Adaptation and Random Guided Filter: Détection améliorée des têtes de blé dans les images à l’aide de l’adaptation du domaine Fourier et du filtre guidé aléatoire – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Sylvester+C%2E+Okafor%22">Sylvester C. Okafor</searchLink><br /><searchLink fieldCode="AR" term="%22Linjing+Wei%22">Linjing Wei</searchLink><br /><searchLink fieldCode="AR" term="%22Solomon+Boamah%22">Solomon Boamah</searchLink><br /><searchLink fieldCode="AR" term="%22Le+Zhang%22">Le Zhang</searchLink><br /><searchLink fieldCode="AR" term="%22Mamadou+B%2E+Diallo%22">Mamadou B. Diallo</searchLink> – Name: TitleSource Label: Source Group: Src Data: Canadian Journal of Remote Sensing, Vol 50, Iss 1 (2024) – Name: Publisher Label: Publisher Information Group: PubInfo Data: Taylor & Francis Group, 2024. – Name: DatePubCY Label: Publication Year Group: Date Data: 2024 – Name: Subset Label: Collection Group: HoldingsInfo Data: LCC:Environmental sciences<br />LCC:Technology – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Environmental+sciences%22">Environmental sciences</searchLink><br /><searchLink fieldCode="DE" term="%22GE1-350%22">GE1-350</searchLink><br /><searchLink fieldCode="DE" term="%22Technology%22">Technology</searchLink> – Name: Abstract Label: Description Group: Ab Data: Wheat head detection is essential in estimating the important characteristics of wheat. However, detecting wheat heads in images from different domains has been challenging due to variations in domain features and environmental conditions. This research aims to improve the robustness of wheat head detection in wheat images. A combination of Fourier domain adaptation (FDA), adaptive alpha beta gamma correction (AABG) and random guided filter (RGF) preprocessing methods was applied in this study. The authors utilized FDA to reduce variations between different domains by transforming an image into the Fourier domain, aligning its distribution with a randomly selected image of another domain. AABG adjusts image properties based on local statistics of the image patches, and RGF, a technique for edge-aware image filtering, was used as augmentation. An EfficientDet model was trained on the publicly available wheat dataset and the results were analyzed and compared to a baseline model. The FDA + RGF approach achieved an improved mean average precision (mAP) of 0.6534 compared to the baseline mAP of 0.6292. Our study can contribute to advancing wheat head detection techniques in agriculture, addressing factors like variations in wheat head appearance by focusing on improving domain variation through data dependent approaches. – Name: TypeDocument Label: Document Type Group: TypDoc Data: article – Name: Format Label: File Description Group: SrcInfo Data: electronic resource – Name: Language Label: Language Group: Lang Data: English<br />French – Name: ISSN Label: ISSN Group: ISSN Data: 1712-7971<br />07038992 – Name: NoteTitleSource Label: Relation Group: SrcInfo Data: https://doaj.org/toc/1712-7971 – Name: DOI Label: DOI Group: ID Data: 10.1080/07038992.2024.2367479 – Name: URL Label: Access URL Group: URL Data: <link linkTarget="URL" linkTerm="https://doaj.org/article/a5db1ad37b3b42a1b24693b0609bbbe4" linkWindow="_blank">https://doaj.org/article/a5db1ad37b3b42a1b24693b0609bbbe4</link> – Name: AN Label: Accession Number Group: ID Data: edsdoj.5db1ad37b3b42a1b24693b0609bbbe4 |
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