[BibTeX] [RIS]
Improved and Generalized Vine Line Detection on Aerial Images Using Asymmetrical Neural Networks and ML Subclassifiers
Type of publication: Inproceedings
Citation:
Year: 2021
Month: October
Publisher: i6doc.com publ.
Location: Bruges, Belgium (Online)
Organization: ESANN
ISBN: 978287587082-7
Abstract: 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.
Keywords:
Authors Treboux, Jerome
Ingold, Rolf
Genoud, Dominique
Added by: []
Total mark: 0
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