| Popis: |
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 labor, 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%. Processing speed was increased by over 500%, and the time required for the manual processing component was reduced by 8.4 ×. 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. |
