%Aigaion2 BibTeX export from HES SO Valais Publications
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@INPROCEEDINGS{,
        author = {Treboux, Jerome and Ingold, Rolf and Genoud, Dominique},
         month = oct,
         title = {Improved and Generalized Vine Line Detection on Aerial Images Using Asymmetrical Neural Networks and ML Subclassifiers},
          year = {2021},
     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.}
}