Abstract
Smoke detection is the key to industrial safety warnings and fire prevention, such as flare smoke detection in chemical plants and forest fire warning. Due to the complex changes in smoke color, texture and shape, it is difficult to identify the smoke in the image. Recently, more and more scholars have paid attention to the research of smoke detection. In order to solve the above problems, we propose a convolutional neural network structure designed for smoke characteristics. The characteristics of smoke are only complicated in simple features, and no deep semantic structure information needs to be extracted. Therefore, there is no performance improvement in deepening the depth of the network. We use a 10-layer convolutional neural network to hop the features of the first layer of convolution extraction to the back layer to increase the network’s ability to extract simple features. The experimental results show that our convolutional neural network model has fewer parameters than the existing deep learning method, and the accuracy rate in the smoke database is optimal.
This work was supported in part by the 18 Connotation Development Quota - Key Discipline - Advanced Manufacturing Discipline Group - Faculty of Information Technology of Beijing University of Technology (Grant 040000514118032), the National Science Foundation of China (Grants 61703009), the Young Elite Scientist Sponsorship Program by China Association for Science and Technology (Grant 2017QNRC001), and Young Top-Notch Talents Team Program of Beijing Excellent Talents Funding (Grant 2017000026833ZK40).
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Liu, M., Gu, K., Wu, L., Xu, X., Qiao, J. (2019). Learn a Deep Convolutional Neural Network for Image Smoke Detection. In: Zhai, G., Zhou, J., An, P., Yang, X. (eds) Digital TV and Multimedia Communication. IFTC 2018. Communications in Computer and Information Science, vol 1009. Springer, Singapore. https://doi.org/10.1007/978-981-13-8138-6_18
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DOI: https://doi.org/10.1007/978-981-13-8138-6_18
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