  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.
