Résumé

It is widely accepted that deep neural networks are very effi- cient for detecting objects in images. They reach their limit when detect- ing 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 archi- tecture 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.

Détails

Actions

PDF