Multiple Categories Of Visual Smoke Detection Database

TL;DR

This paper introduces a new multi-category smoke detection database with over 70,000 images, addressing the limitations of previous datasets by including different smoke types relevant to industrial scenarios.

Contribution

The creation of a comprehensive multi-category smoke detection dataset that better reflects real-world industrial conditions and the evaluation of existing models on this new database.

Findings

Current algorithms need improvement on the new dataset.

The proposed database effectively captures diverse smoke types.

Models show varied performance across different smoke categories.

Abstract

Smoke detection has become a significant task in associated industries due to the close relationship between the petrochemical industry's smoke emission and its safety production and environmental damage. There are several production situations in the real industrial production environment, including complete combustion of exhaust gas, inadequate combustion of exhaust gas, direct emission of exhaust gas, etc. We discovered that the datasets used in previous research work can only determine whether smoke is present or not, not its type. That is, the dataset's category does not map to the real-world production situations, which are not conducive to the precise regulation of the production system. As a result, we created a multi-categories smoke detection database that includes a total of 70196 images. We further employed multiple models to conduct the experiment on the proposed database, the results show that the performance of the current algorithms needs to be improved and demonstrate the effectiveness of the proposed database.