Remote Monitoring of Amur Tigers in Forest Ecosystems Using Improved YOLOX Algorithm

Abstract

In response to the challenge of collecting behavioral data on Amur tigers living in forests, a remote real-time data collection approach is proposed. In this article, a novel attention mechanism named CBAM-E is introduced, and CBAM-E as well as the CIoU loss function are incorporated into the YOLOX object detection algorithm, resulting in a new YOLOX model. The new model demonstrates significant performance improvements over the original model, with the mAP0.5 detection accuracy metric rising from 97.32 to 98.18%, indicating a boost of 0.86%, and the mAP0.75 metric increasing from 75.10 to 78.70%, marking an enhancement of 3.60%. The enhanced algorithm is subsequently applied to remote terminal information collection, offering a reference for detection algorithms in the study of wild behaviors of Amur tigers in forests, biodiversity conservation, and the collection of related field data about Amur tigers in the wild.