Deep learning with self-supervision and uncertainty regularization to count fish in underwater images.

ABSTRACT: Effective conservation actions require effective population monitoring. However, accurately counting animals in the wild to inform conservation decision-making is difficult. Monitoring populations through image sampling has made data collection cheaper, wide-reaching and less intrusive but created a need to process and analyse this data efficiently. Counting animals from such data is challenging, particularly when densely packed in noisy images. Attempting this manually is slow and expensive, while traditional computer vision methods are limited in their generalisability. Deep learning is the state-of-the-art method for many computer vision tasks, but it has yet to be properly explored to count animals. To this end, we employ deep learning, with a density-based regression approach, to coun

SUBMITTER: Tarling P 

PROVIDER: S-EPMC9067705 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature