Uncertainty-Aware Post-Detection Framework for Enhanced Fire and Smoke Detection in Compact Deep Learning Models
This work addresses reliability issues in fire and smoke detection for safety applications using compact models on UAVs and IoT devices, representing an incremental improvement over existing post-detection methods.
The paper tackled the problem of false positives and missed detections in compact deep learning models for fire and smoke detection by proposing an uncertainty-aware post-detection framework that rescales detection confidences using statistical uncertainty and visual cues. Experiments on the D-Fire dataset showed improved precision, recall, and mean average precision with modest computational overhead.
Accurate fire and smoke detection is critical for safety and disaster response, yet existing vision-based methods face challenges in balancing efficiency and reliability. Compact deep learning models such as YOLOv5n and YOLOv8n are widely adopted for deployment on UAVs, CCTV systems, and IoT devices, but their reduced capacity often results in false positives and missed detections. Conventional post-detection methods such as Non-Maximum Suppression and Soft-NMS rely only on spatial overlap, which can suppress true positives or retain false alarms in cluttered or ambiguous fire scenes. To address these limitations, we propose an uncertainty aware post-detection framework that rescales detection confidences using both statistical uncertainty and domain relevant visual cues. A lightweight Confidence Refinement Network integrates uncertainty estimates with color, edge, and texture features to adjust detection scores without modifying the base model. Experiments on the D-Fire dataset demonstrate improved precision, recall, and mean average precision compared to existing baselines, with only modest computational overhead. These results highlight the effectiveness of post-detection rescoring in enhancing the robustness of compact deep learning models for real-world fire and smoke detection.