A Multi-Stage Computer Vision Framework for Real-Time Fire Detection in Low-Light Multi-Camera Environments

This paper presents NexFire Pro, a deployable desktop framework for real-time fire and smoke surveillance across multiple simultaneous IP or USB camera streams. The research contribution is a failure-mode-driven pipeline design methodology: each of the five post-detection stages is derived from a distinct, identifiable failure-mode category (illumination drift, static false-positive objects, spatially expected heat sources, temporal noise), evaluated with a leakage-free, source-disjoint held-out protocol that avoids the temporal data leakage that frame-level splits introduce. A fine-tuned YOLO11s backbone serves as the fixed detection substrate, surrounded by a five-stage processing pipeline: (1) CLAHE and adaptive gamma correction; (2) neural detection; (3) MOG2-based motion validation; (4) normalised-coordinate ROI exclusion; and (5) temporal alarm hysteresis. On a leakage-free held-out image test set, the detector attains mAP@0.5 = 0.65 (F1 = 0.70). A leave-one-out ablation on non-fire video shows that motion validation is the dominant false-alarm suppressor, reducing a raw-detector baseline of eight false alarms to zero. The proposed design-and-evaluation framework is intended to be transferable to other reliability-oriented computer vision applications beyond fire detection.