Improving the accessibility and transferability of machine learning algorithms for identification of animals in camera trap images: MLWIC2.

ABSTRACT: Motion-activated wildlife cameras (or "camera traps") are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera trap images from 18 studies in 10 states across the United States of America to train two deep neural networks

SUBMITTER: Tabak MA 

PROVIDER: S-EPMC7548173 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature