Real-Time Threaded Houbara Detection and Segmentation for Wildlife Conservation using Mobile Platforms
This work addresses the problem of efficient, non-invasive monitoring for conservationists by providing a real-time solution on mobile platforms, though it is incremental as it builds on existing YOLO and SAM methods with threading for performance gains.
The paper tackles real-time detection and segmentation of the cryptic Houbara Bustard for wildlife conservation by proposing a mobile-optimized two-stage deep learning framework, achieving a mAP50 of 0.9627 and mIoU of 0.7421 with YOLOv10 operating at 43.7 ms per frame.
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.