针对火焰烟雾透明特性，提出新型检测模型提升火灾识别精度。
Fire and Smoke Detection with Burning Intensity Representation
- 设计专注透明目标的注意力检测头，优化火焰烟雾定位
- 引入燃烧强度特征，支持后续风险评估任务
- 在多个数据集上验证有效性，适合实际安防场景
由于火灾灾害的破坏性，高效的火灾与烟雾检测（FSD）及分析系统至关重要。然而，现有方法多直接采用通用目标检测技术，未考虑火焰与烟雾的透明特性，导致定位不准确、检测性能下降。为此，本文提出一种注意力火灾烟雾检测模型（a-FSDM），该模型在保留传统检测算法鲁棒特征提取与融合能力的基础上，专门重构了检测头，称为注意力透明度检测头（ATDH），以应对透明目标挑战。同时，引入燃烧强度（BI）作为火灾相关下游风险评估的关键特征。在多个FSD数据集上的大量实验表明，所提模型具有显著的有效性与通用性。
原文摘要 · Abstract (English)
An effective Fire and Smoke Detection (FSD) and analysis system is of paramount importance due to the destructive potential of fire disasters. However, many existing FSD methods directly employ generic object detection techniques without considering the transparency of fire and smoke, which leads to imprecise localization and reduces detection performance. To address this issue, a new Attentive Fire and Smoke Detection Model (a-FSDM) is proposed. This model not only retains the robust feature extraction and fusion capabilities of conventional detection algorithms but also redesigns the detection head specifically for transparent targets in FSD, termed the Attentive Transparency Detection Head (ATDH). In addition, Burning Intensity (BI) is introduced as a pivotal feature for fire-related downstream risk assessments in traditional FSD methodologies. Extensive experiments on multiple FSD datasets showcase the effectiveness and versatility of the proposed FSD model. The project is available at \href{https://xiaoyihan6.github.io/FSD/}{https://xiaoyihan6.github.io/FSD/}.
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