Improved and Generalized Vine Line Detection on Aerial Images Using Asymmetrical Neural Networks and ML Subclassifiers
| Art der Publikation: | Artikel in einem Konferenzbericht |
| Zitat: | |
| Jahr: | 2021 |
| Monat: | Oktober |
| Verlag: | i6doc.com publ. |
| Ort: | Bruges, Belgium (Online) |
| Organisation: | ESANN |
| ISBN: | 978287587082-7 |
| Abriss: | It is widely accepted that deep neural networks are very efficient for detecting objects in images. They reach their limit when detecting multiple instances of long lines in low-resolution images. We present an original methodology for the recognition of vine lines in low-resolution satellite images. The method consists in combining an asymmetrical neural network with a sub-classifier. We first compare a traditional U-Net architecture with an asymmetrical U-Net architecture designed for precision agriculture. We then highlight the significant improvement in vine line detection when a Random Forest is added after the customized U-Net. This methodology addresses the complex task of dissociating vine lines from other agricultural objects. As a result, our experiments improve the precision from 0.83 to 0.94 over our optimized neural network. |
| Schlagworte: | |
| Autoren | |
| Hinzugefügt von: | [] |
| Gesamtbewertung: | 0 |
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