CameraTrapDetector: detect, classify, and count animals in camera trap images using deep learning.

Music City Center 

Camera traps are a widespread, non-invasive, cost-effective method to monitor animal populations; researchers using camera traps comprise diverse disciplines and geographies. The time and labour required to manually classify potentially millions of images generated by a single camera array presents a significant challenge. Reducing this burden facilitates implementation of larger, longer-lasting camera trap arrays, resulting in more comprehensive analyses and better decision frameworks. To address this challenge, a multi-agency USDA team has developed CameraTrapDetector - a free, open-source tool that deploys computer vision models at the class, family, and species (Nclasses=63, mAP(50-95)=0.878, F1=0.919) taxonomic levels to detect, classify, and count animals in camera trap images. The tool is available as an R package with a R Shiny interface, a desktop application, or a command-line Python script for easy integration into many analytical pipelines. The tool enables users to retain complete data privacy, and developers maintain a transdisciplinary, multi-institutional working group of camera trap researchers to advance best practices. An iterative training cycle uses state-of-the-art computer vision approaches and adds new images from project partners to train new models and incorporates user feedback and goals into the tool's development. A primary goal, and challenge, for the models is generalization to out of site images; results are less accurate, and more variable, compared to metrics for test (unseen) in-site images. Results on test data (Nclasses=12) show major improvements in generalization from the version 2 model (mAR = 0.195, range 0.07-0.98) to the version 3 model (mAR = 0.606, range 0.05-1.00). Faster, more accurate, more generalizable models allow CameraTrapDetector users to turn raw images into quantifiable data for answering questions about estimating animal presence, population size, and movement. Our open-source pipeline may also be leveraged to train species-specific computer vision models to answer questions about animal behaviour or disease detection. By automating image processing, CameraTrapDetector streamlines research speed and redirects critical human resources to more analytical research tasks.

Computer Vision

Deep Learning

Artificial Intelligence

Animal Behavior

Image Processing