Real-time habitat mapping with YOLOv8: A multi-threaded approach to biodiversity preservation

This paper presents a robust system for real-time object detection and counting in ecological video streams. It is based on the YOLOv8 architecture integrated within a multi-threaded video processing architecture. The system reduces latency and improves throughput by parallelizing object detection and preprocessing tasks. This leads to outperforming traditional single-threaded implementations in continuous video analysis. The system also incorporates dynamic thresholding methods, fine-tuning, and data augmentation to enhance object detection reliability in dynamic natural environments. These mechanisms improve robustness to changing lighting, occlusions, and background complexity, common challenges in outdoor footage. The system is thoroughly evaluated through performance comparisons between multi-threaded and single-threaded implementations, environmental stress tests, and an ablation study. Results demonstrate improved consistency in object detection and counting in dynamic environments, along with significant gains in processing speed. Designed for deployment on lightweight and low-power devices, the system is suitable for remote or resource-constrained settings. While designed for biodiversity monitoring, the approach is applicable to other domains requiring efficient, real-time video analysis in unstructured environments. • The multithreaded version doubles the frame rate of the single-threaded baseline. • Dynamic thresholding improves object detection under occlusion and lighting changes. • Region-based counting reduces duplicate object detections in dense natural scenes. • The system reaches 90% accuracy on unlabeled ecological video using low-cost devices.