Multiple Categories Of Visual Smoke Detection Database
This work addresses the need for precise smoke type detection in the petrochemical industry to enhance safety and environmental regulation, but it is incremental as it focuses on dataset creation rather than algorithmic advancement.
The authors tackled the problem of smoke detection in industrial settings by creating a new database with 70,196 images that categorizes smoke types based on real-world production situations, and they found that current algorithms perform poorly on this dataset, highlighting the need for improvement.
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.