An approach to rapid processing of camera trap images with minimal human input.

ABSTRACT: Camera traps have become an extensively utilized tool in ecological research, but the manual processing of images created by a network of camera traps rapidly becomes an overwhelming task, even for small camera trap studies.We used transfer learning to create convolutional neural network (CNN) models for identification and classification. By utilizing a small dataset with an average of 275 labeled images per species class, the model was able to distinguish between species and remove false triggers.We trained the model to detect 17 object classes with individual species identification, reaching an accuracy up to 92% and an average F1 score of 85%. Previous studies have suggested the need for thousands of images of each object class to reach results comparable to those achieved by human obse

SUBMITTER: Duggan MT 

PROVIDER: S-EPMC8427629 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature