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- W4387376492 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." @default.
- W4387376492 created "2023-10-06" @default.
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- W4387376492 date "2023-10-05" @default.
- W4387376492 modified "2023-10-16" @default.
- W4387376492 title "Remote Monitoring of Amur Tigers in Forest Ecosystems Using Improved YOLOX Algorithm" @default.
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- W4387376492 doi "https://doi.org/10.3390/f14102000" @default.
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