论文标题
多类视觉烟雾检测数据库
Multiple Categories Of Visual Smoke Detection Database
论文作者
论文摘要
由于石化行业的烟雾排放与安全生产和环境破坏之间的密切关系，烟雾检测已成为相关行业的重要任务。在实际的工业生产环境中，有几种生产情况，包括完全燃烧废气，排气的燃烧不足，排气的直接排放等。我们发现，先前研究工作中使用的数据集只能确定是否存在烟雾，而不是其类型。也就是说，数据集的类别不映射到现实世界中的生产情况，这些情况不利于生产系统的精确调节。结果，我们创建了一个多类烟雾检测数据库，其中包括70196张图像。我们进一步采用了多个模型来对提出的数据库进行实验，结果表明，需要改进当前算法的性能，并证明提出的数据库的有效性。
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.
