A Deterministic Edge-AI System for Early Wildfire Smoke Detection: From Lightweight Neural Models to Operationally Reliable Surveillance

Wildfire mitigation depends critically on minimizing Time-to-Detect (TTD). While lightweight convolutional neural networks (CNNs) enable visual detection of early-stage wildfire smoke, deploying them in practical, autonomous off-grid monitoring towers remains a system engineering challenge due to strict power and hardware constraints. This paper presents a proof-of-concept (PoC) implementation of a deterministic Edge-AI architecture designed for reliable wildfire surveillance. The proposed approach introduces an application-layer scheduler guaranteeing bounded system latency, as well as a hybrid approach connecting neural network output with heuristic spatio-temporal logic reducing false positives. Preliminary experimental results demonstrate that the proposed system achieves an F1-Score of 92.5% while maintaining stable latency for up to nine concurrent streams, proving that a multi-camera wildfire detection is achievable in practice, also on low-power edge hardware.