An automatic method for removing empty camera trap images using ensemble learning.

ABSTRACT: Camera traps often produce massive images, and empty images that do not contain animals are usually overwhelming. Deep learning is a machine-learning algorithm and widely used to identify empty camera trap images automatically. Existing methods with high accuracy are based on millions of training samples (images) and require a lot of time and personnel costs to label the training samples manually. Reducing the number of training samples can save the cost of manually labeling images. However, the deep learning models based on a small dataset produce a large omission error of animal images that many animal images tend to be identified as empty images, which may lead to loss of the opportunities of discovering and observing species. Therefore, it is still a challenge to build the DCNN model w

SUBMITTER: Yang DQ 

PROVIDER: S-EPMC8216933 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature