eBirdNet-Nano: An Operator-Aware Lightweight Detector and Edge AI Terminal for Endangered Bird Real-Time Monitoring

Real-time monitoring of endangered birds on edge AI hardware is constrained by a structural mismatch between modern lightweight detectors and mainstream NPU deployment toolchains. Recent attention-based detectors rely heavily on dynamic-shape operators that fall back to the host CPU on embedded NPUs, negating the advantages of lightweight architectures. To address this, we propose eBirdNet-Nano, a 1.05 M-parameter detector derived from YOLOv12n through a three-level NPU-friendly redesign: a static NPUConv block at the operator level, an NPU-C3k2 module together with an NPU-SE-Block at the module level, and a balanced 64-channel detection head at the head level. The resulting model achieves a 59% parameter reduction over YOLOv12n at only 5.8 GFLOPs while attaining an mAP@0.5 of 0.929 on a curated 24-species endangered-bird dataset collected in Macao. We further evaluate the model across four heterogeneous edge platforms—the Rockchip RK3588 (ARM + NPU), Kendryte K230 (RISC-V + KPU), Raspberry Pi 4B (pure ARM), and LicheePi 4A (pure RISC-V)—to characterize its behavior under distinct execution models. On the RK3588 NPU under INT8 quantization, eBirdNet-Nano delivers 13.83 ms inference latency and 26.76 ms end-to-end latency at 37.4 FPS, attaining the best parameter–latency balance and the highest parameter-normalized throughput (35.62 FPS/M) among six nano-scale YOLO variants, with an overall 3.53× end-to-end speedup over the YOLOv12n FP16 baseline that decomposes into a 2.97× architectural factor and a 1.19× quantization factor. Integrated into the EbirdEye field terminal, the same model sustains 23.5 ms thread-level end-to-end latency during live operation while supporting approximately 13.5 h of battery-powered runtime per charge. The proposed design offers a practical pathway toward deployable, low-power AI terminals for endangered-species conservation in resource-constrained field environments.