Abstract
Fire outbreaks pose critical threats to human life and infrastructure, necessitating high-fidelity early-warning systems that detect combustion precursors such as smoke. However, smoke plumes exhibit complex spatiotemporal dynamics influenced by illumination variability, flow kinematics, and environmental noise, undermining the reliability of traditional detectors. To address these challenges without the logistical complexity of multi-sensor arrays, we propose an information-fusion framework by integrating smoke feature representations extracted from monocular imagery. Specifically, a Two-Phase Uncertainty-Aware Shifted Windows Transformer for robust and reliable smoke detection, leveraging a novel smoke segmentation dataset, constructed via optical flow-based motion encoding, is proposed. The optical flow estimation is performed with a four-color-theorem-inspired dual-phase level-set fractional-order variational model, which preserves motion discontinuities. The resulting color-encoded optical flow maps are fused with appearance cues via a Gaussian Mixture Model to generate binary segmentation masks of the smoke regions. These fused representations are fed into the novel Shifted-Windows Transformer, which is augmented with a multi-scale uncertainty estimation head and trained under a two-phase learning regimen. First learning phase optimizes smoke detection accuracy, while during the second phase, the model learns to estimate plausibility confidence in its predictions by jointly modeling aleatoric and epistemic uncertainties. Extensive experiments using multiple evaluation metrics and comparative analysis with state-of-the-art approaches demonstrate superior generalization and robustness, offering a reliable solution for early fire detection in surveillance, industrial safety, and autonomous monitoring applications.
Abstract (translated)
火灾爆发对人类生命和基础设施构成重大威胁，需要高精度的早期预警系统来检测如烟雾等燃烧前兆。然而，烟羽表现出复杂的时空动态特性，受到照明变化、流体动力学以及环境噪声的影响，从而降低了传统探测器的可靠性。为了解决这些问题而不增加多传感器阵列的复杂性，我们提出了一种信息融合框架，通过将单目图像中的烟雾特征表示进行整合来实现这一目标。具体来说，我们提出了一个两阶段不确定性感知移位窗口变换模型（Two-Phase Uncertainty-Aware Shifted Windows Transformer），用于鲁棒且可靠的烟雾检测，并利用基于光流编码的新型烟雾分割数据集来构建该模型。光学流动估计使用一种受四色定理启发的双相水平集合分数阶变分模型完成，这种模型能够保留运动间的不连续性。 生成的颜色编码光流图通过高斯混合模型与外观线索相结合，生成烟雾区域的二值分割掩模。这些融合后的表示形式被输入到新的移位窗口变换器中，该变换器配备了多尺度不确定性估计头，并在两阶段学习方案下进行训练。第一阶段优化烟雾检测精度；第二阶段则通过共同建模随机不确定性和系统不确定性来学习对其预测的可能性信心。 使用多种评估指标及与现有最佳方法的比较分析显示，该方法具有优越的一般化能力和鲁棒性，在监控、工业安全和自主监测应用中早期火灾检测方面提供了一种可靠的解决方案。
URL
https://arxiv.org/abs/2508.14597
