Fire and Smoke Detection with Burning Intensity Representation

TL;DR

This paper introduces a novel fire and smoke detection model that incorporates burning intensity as a key feature and employs an attentive detection head tailored for transparent targets, improving localization and detection accuracy.

Contribution

The paper proposes a new Attentive Fire and Smoke Detection Model with a specialized detection head and burning intensity feature, enhancing detection performance for transparent fire and smoke targets.

Findings

Effective detection accuracy demonstrated on multiple datasets

Burning Intensity improves downstream risk assessment

Model outperforms existing detection methods

Abstract

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/}.