Efficient Wild Animal Detection and Collection Using Quantized Models on Low-End Edge Devices

Vision equipment plays a crucial role in wildlife conservation by enabling the detection and collection of wild animal images, thereby providing an efficient way for preserving biodiversity observation. However, traditional manual detection methods are inefficient and costly. While cloud server-based methods offer an alternative, they introduce challenges such as transmission delays and data security concerns. To address these limitations, we propose an edge computing-based AI vision terminal for autonomous wildlife monitoring. Evaluations using the NCNN framework and varying input resolutions (640,320, 160 pixels) revealed that YOLOv8n models resulted in significantly faster inference times (up to 7.8-14.5x speedup at 160 pixels compared to 640 pixels). We implemented and evaluated quantized YOLOv8n and YOLOv8s models using NCNN on a Raspberry Pi, achieving significant inference speedups (18.840.1% reduction in inference time) compared to non-quantized models across various image sizes. YOLOv8s-int8 offered a better speed-precision trade-off ($24 \%$ faster for $6.8 \%$ lower precision) than YOLOv8n-int8 ($\mathbf{1 2 . 8 \%}$ faster for $\mathbf{1 1 . 6 \%}$ lower precision). This approach enables real-time animal detection with approximately 3 W power consumption, demonstrating the feasibility of deploying intelligent wildlife monitoring systems in remote, resource-constrained environments. Furthermore, the edge device exhibits robust detection performance for complex backgrounds and small targets.