用手机实时检测并分割濒危石鸡，提升野生动物保护效率。
Real-Time Threaded Houbara Detection and Segmentation for Wildlife Conservation using Mobile Platforms
- 采用线程化YOLOv10与MobileSAM并行处理，降低延迟。
- 在石鸡数据集上达到mAP50 0.9627，每帧仅43.7毫秒。
- 专为移动设备优化，适合野外实时监测场景。
自然环境中实时动物检测与分割对野生动物保护至关重要，可通过远程相机流实现非侵入式监测。然而，受限于计算资源及许多物种隐蔽的外观特征，该任务仍具挑战性。本文提出一种面向移动平台的两阶段深度学习框架，集成线程化检测模型（TDM），并行执行基于YOLOv10的检测与基于MobileSAM的轻量化分割。相较于以往的YOLO+SAM流水线，本方法通过线程化设计降低延迟。YOLOv10负责检测，MobileSAM执行分割，两者并发运行以提高资源利用率。在重点保护物种——石鸡上，模型取得mAP50 0.9627、mAP75 0.7731、mAP95 0.7178，以及MobileSAM mIoU 0.7421的性能。YOLOv10每帧处理耗时43.7毫秒，证实具备实时能力。我们构建了一个包含4万张标注图像的石鸡专用数据集，支持跨多种环境下的训练与评估。相关代码与数据集已开源至GitHub：https://github.com/LyesSaadSaoud/mobile-houbara-detseg。互动演示及其他资源请访问：https://lyessaadsaoud.github.io/LyesSaadSaoud-Threaded-YOLO-SAM-Houbara。
原文摘要 · Abstract (English)
Real-time animal detection and segmentation in natural environments are vital for wildlife conservation, enabling non-invasive monitoring through remote camera streams. However, these tasks remain challenging due to limited computational resources and the cryptic appearance of many species. We propose a mobile-optimized two-stage deep learning framework that integrates a Threading Detection Model (TDM) to parallelize YOLOv10-based detection and MobileSAM-based segmentation. Unlike prior YOLO+SAM pipelines, our approach improves real-time performance by reducing latency through threading. YOLOv10 handles detection while MobileSAM performs lightweight segmentation, both executed concurrently for efficient resource use. On the cryptic Houbara Bustard, a conservation-priority species, our model achieves mAP50 of 0.9627, mAP75 of 0.7731, mAP95 of 0.7178, and a MobileSAM mIoU of 0.7421. YOLOv10 operates at 43.7 ms per frame, confirming real-time readiness. We introduce a curated Houbara dataset of 40,000 annotated images to support model training and evaluation across diverse conditions. The code and dataset used in this study are publicly available on GitHub at https://github.com/LyesSaadSaoud/mobile-houbara-detseg. For interactive demos and additional resources, visit https://lyessaadsaoud.github.io/LyesSaadSaoud-Threaded-YOLO-SAM-Houbara.
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