| WU Jin(吴进),DAI Wei,WANG Yu,ZHAO Bo.[J].高技术通讯(英文),2022,28(4):401~410 |
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| Design and implementation of gasifier flame detection system based on SCNN |
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| DOI：10.3772/j.issn.1006-6748.2022.04.008 |
| 中文关键词: |
| 英文关键词: support vector machine convolutional neural network (SCNN), support vector machine (SVM), flame detection, flame image processing, gasifier |
| 基金项目: |
| Author Name | Affiliation | | WU Jin(吴进) | (School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, P.R.China) | | DAI Wei | (School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, P.R.China) | | WANG Yu | (School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, P.R.China) | | ZHAO Bo | (School of Electronic Engineering, Xi’an University of Posts and Telecommunications, Xi’an 710121, P.R.China) |
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| 中文摘要: |
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| 英文摘要: |
| Flame detection is a research hotspot in industrial production, and it has been widely used in various fields. Based on the ignition and combustion video sequence, this paper aims to improve the accuracy and unintuitive detection results of the current flame detection methods of gasifier and industrial boiler. A furnace flame detection model based on support vector machine convolutional neural network (SCNN) is proposed. This algorithm uses the advantages of neural networks in the field of image classification to process flame burning video sequences which needs detailed analysis. Firstly, the support vector machine (SVM) with better small sample classification effect is used to replace the Softmax classification layer of the convolutional neural network (CNN) network. Secondly, a Dropout layer is introduced to improve the generalization ability of the network. Subsequently, the area, frequency and other important parameters of the flame image are analyzed and processed. Eventually, the experimental results show that the flame detection model designed in this paper is more accurate than the CNN model, and the accuracy of the judgment on the flame data set collected in the gasifier furnace reaches 99.53%. After several ignition tests, the furnace flame of the gasifier can be detected in real time. |
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