Active learning for efficient annotation in precision agriculture: a use-case on crop-weed semantic segmentation

arXiv:2404.02580v1 Announce Type: new
Abstract: Optimizing deep learning models requires large amounts of annotated images, a process that is both time-intensive and costly. Especially for semantic segmentation models in which every pixel must be annotated. A potential strategy to mitigate annotation effort is active learning. Active learning facilitates the identification and selection of the most informative images from a large unlabelled pool. The underlying premise is that these selected images can improve the model's performance faster than random selection to …

abstract active learning agriculture annotation arxiv case cs.ai cs.cv deep learning every images pixel precision process segmentation semantic strategy type