Detecting Wildfire Smoke Onset from Fixed View Camera Networks

(1) Background: A wildfire smoke detector’s practical worth is counted in minutes, not percentage points: an alarm raised ten minutes after ignition is worth far less than one raised at two, whatever the two systems’ frame-level scores suggest. Time to detection, however, is almost always computed after training rather than optimized during it. (2) Methods: We treat incipient smoke not as an object to be recognized but as an onset to be caught: a faint, spatially coherent structure that thickens monotonically. At the same time, the rest of the scene continues behaving as it always has. The proposed study learns that habitual behavior from unlabeled fire-free video, surfaces departures from it as a per-tile prediction residual, and reads those residuals through an emergence field of four inspectable cues: residual magnitude, opacity accretion, motion coherence, and temporal persistence. Each common distractor fails a different cue, and detection delay and false alarms directly contribute to the loss. (3) Results: On FIgLib, the proposed study raises its first alarm 1.52 min after ignition while holding 0.21 false alarms per camera-day, and carries over to PyroNear-2025 and SKLFS-WildFire without labels from either. (4) Conclusions: Treating earliness as an optimization target rather than a reporting statistic yields detectors that stay both early and quiet on cameras they never saw in training.