Uncertainty Driven Active Learning for Image Segmentation in Underwater Inspection

arXiv:2403.14002v1 Announce Type: cross
Abstract: Active learning aims to select the minimum amount of data to train a model that performs similarly to a model trained with the entire dataset. We study the potential of active learning for image segmentation in underwater infrastructure inspection tasks, where large amounts of data are typically collected. The pipeline inspection images are usually semantically repetitive but with great variations in quality. We use mutual information as the acquisition function, calculated using Monte Carlo dropout. …

abstract active learning arxiv cs.cv cs.lg data dataset image infrastructure segmentation study tasks train type uncertainty underwater