Forest fire smoke recognition based on convolutional neural network
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ORIGINAL PAPER
Forest fire smoke recognition based on convolutional neural network Xiaofang Sun1 · Liping Sun1 · Yinglai Huang1
Received: 25 March 2020 / Accepted: 23 May 2020 © Northeast Forestry University 2020
Abstract Traditional fire smoke detection methods mostly rely on manual algorithm extraction and sensor detection; however, these methods are slow and expensive to achieve discrimination. We proposed an improved convolutional neural network (CNN) to achieve fast analysis. The improved CNN can be used to liberate manpower. The network does not require complicated manual feature extraction to identify forest fire smoke. First, to alleviate the computational pressure and speed up the discrimination efficiency, kernel principal component analysis was performed on the experimental data set. To improve the robustness of the CNN and to avoid overfitting, optimization strategies were applied in multi-convolution kernels and batch normalization to improve loss functions. The experimental analysis shows that the CNN proposed in this study can learn the feature information automatically for smoke images in the early stages of fire automatically with a high recognition rate. As a result, the improved CNN enriches the theory of smoke discrimination in the early stages of a forest fire. Keywords Forest fire smoke · Convolutional neural network · Image classification · Kernel principal component analysis Project funding: This work was supported by National Natural Science Foundation of China (31670717) and Natural Science Foundation of Heilongjiang Province (LH2020C051). The online version is available at http://www.springerlink.com Corresponding editor: Yu Lei * Liping Sun [email protected] 1
Northeast Forestry University, Harbin 150040, People’s Republic of China
Introduction China is a large forestry country with several natural forest resources and planted forest plantations with management areas at the forefront of the world. However, forest fires are extremely destructive and are accompanied by many complex physical phenomena. In the early stages of a forest fire, there is smoke and fire plumes. Smoke spreads faster than flames, and combustion is often restricted to solid combustibles. Early stages of forest fires have the characteristics of slow burning and are difficult to detect. This often leads to many major fires; and smoke detection has greater value than flame detection. If the smoke can be spotted quickly and the fire can be put out, this can save considerable forest resources as well as lives and properties. As a result, smoke is the basis of forest fire detection. The sooner the smoke is detected, the earlier the fire is detected. Therefore, the identification of forest fire smoke is a topic that deserves special attention. Numerous studies have been carried out on the detection of fire smoke. Presently, sensors and manual image feature extraction are commonly used. A large number of sensors can be placed to sample particles of combustion products. If sensors receive enough particles, they ca
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