Use of object detection in camera trap image identification: assessing a method to rapidly and accurately classify human and animal detections for research and application in recreation ecology

Abstract

Camera traps are increasingly used to answer complex ecological questions. However, the rapidly growing number of images collected presents technical challenges. Each image must be classified to extract data, requiring significant labour, and potentially creating an information bottleneck. We applied an object-detection model (MegaDetector) to camera trap data from a study of recreation ecology in British Columbia, Canada. We tested its performance in detecting humans and animals relative to manual image classifications, and assessed efficiency by comparing the time required for manual classification versus a modified workflow integrating object-detection with manual classification. We also evaluated the reliability of using MegaDetector to create an index of human activity for application to the study of recreation impacts to wildlife. In our application, MegaDetector detected human and animal images with 99% and 82% precision, and 95% and 92% recall respectively, at a confidence threshold of 90%. The overall time required to process the dataset was reduced by over 500%, and the manual processing component was reduced by 840%. The index of human detection events from MegaDetector matched the output from manual classification, with a mean 0.45% difference in estimated human detections across site-weeks. Our test of an open-source object-detection model showed it performed well in partially classifying a camera trap dataset, significantly increasing processing efficiency. We suggest that this tool could be integrated into existing camera trap workflows to accelerate research and application by alleviating data bottlenecks, particularly for surveys processing large volumes of human images. We also show how the model and workflow can be used to anonymize human images prior to classification, protecting individual privacy. Impact StatementWe developed and tested a workflow for classifying camera trap images that integrated an existing object-detection model with manual image classification. Our workflow demonstrates an increase in efficiency of 500% over manual labelling, and additionally includes a method to anonymize human images prior to archiving and classification. We provide an example of the application of these tools to ease data processing, particularly for studies focused on recreation ecology which record high volumes of human images. Data lags due to processing delays have the potential to result in sub-optimal conservation decisions, which may be alleviated by accelerated processing. To our knowledge, this is the first in-depth assessment of the practical application of such technology to real world workflows focused on human detections.