Benchmarking Multi-Scene Fire and Smoke Detection

TL;DR

This paper introduces a comprehensive benchmark for fire and smoke detection that standardizes datasets, evaluation, and scenes to accelerate technological progress in real-world applications.

Contribution

It systematically creates and standardizes a new FSD benchmark by expanding and relabeling existing datasets to improve consistency and realism.

Findings

Established a unified FSD benchmark platform

Enhanced dataset diversity and scene coverage

Standardized evaluation protocols

Abstract

The current irregularities in existing public Fire and Smoke Detection (FSD) datasets have become a bottleneck in the advancement of FSD technology. Upon in-depth analysis, we identify the core issue as the lack of standardized dataset construction, uniform evaluation systems, and clear performance benchmarks. To address this issue and drive innovation in FSD technology, we systematically gather diverse resources from public sources to create a more comprehensive and refined FSD benchmark. Additionally, recognizing the inadequate coverage of existing dataset scenes, we strategically expand scenes, relabel, and standardize existing public FSD datasets to ensure accuracy and consistency. We aim to establish a standardized, realistic, unified, and efficient FSD research platform that mirrors real-life scenes closely. Through our efforts, we aim to provide robust support for the breakthrough and development of FSD technology. The project is available at \href{https://xiaoyihan6.github.io/FSD/}{https://xiaoyihan6.github.io/FSD/}.