Abstract
Ecological problems and pollution problems must be faced and solved in the sustainable development of a country. With the continuous development of image analysis technology, it is a good choice to use machine to automatically judge the external environment. In order to solve the problem of smoke extraction and exhaust monitoring, we need the applicable database. Considering the number of databases that can be used to detect smoke is small and these databases have fewer types of pictures, we subdivide the smoke detection database and get a new database for smoke and smoke color detection. The main purpose is to preliminarily identify pollutants in smoke and further develop smoke image detection technology. We discuss eight kinds of convolutional neural network, they can be used to classify smoke images. Testing different convolutional neural networks on this database, the accuracy of several existing networks is analyzed and compared, and the reliability of the database is also verified. Finally, the possible development direction of smoke detection is summarized.
This work is supported by the Major Science and Technology Program for Water Pollution Control and Treatment of China (2018ZX07111005).
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Zhang, H., Chen, J., Li, S., Gu, K., Wu, L. (2020). Smoke Detection Based on Image Analysis Technology. In: Zhai, G., Zhou, J., Yang, H., An, P., Yang, X. (eds) Digital TV and Wireless Multimedia Communication. IFTC 2019. Communications in Computer and Information Science, vol 1181. Springer, Singapore. https://doi.org/10.1007/978-981-15-3341-9_2
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