| 引用本文: | 易佞纯,桂卫华,梁骁俊,张超波,唐峰润,阳春华.挥发窑鼓风管的关键点识别及其摆放位置监测[J].控制理论与应用,2024,41(1):172~182.[点击复制] |
| YI Ning-chun,GUI Wei-hua,LIANG Xiao-jun,ZHANG Chao-bo,TANG Feng-run,YANG Chun-hua.Keypoints identification and position monitoring of the blower pipe for volatilization kiln[J].Control Theory and Technology,2024,41(1):172~182.[点击复制] |
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| 挥发窑鼓风管的关键点识别及其摆放位置监测 |
| Keypoints identification and position monitoring of the blower pipe for volatilization kiln |
| 摘要点击 1290 全文点击 1642 投稿时间：2022-06-17 修订日期：2023-09-19 |
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| DOI编号 10.7641/CTA.2023.20541 |
| 2024,41(1):172-182 |
| 中文关键词 火焰视频 鼓风管位置 关键点检测 卷积神经网络 聚类分析 |
| 英文关键词 flame video the position of blower pipe keypoints detection convolutional neural network cluster analysis |
| 基金项目 国家重点研发计划项目(2019YFB1704703), 国家自然科学基金项目(62103208, 62273362)资助. |
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| 中文摘要 |
| 窑头鼓风管的摆放位置是影响氧化锌挥发窑燃烧状态的重要操作参数之一, 现仍由人工看火来手动调节,
同时现场没有为挥发窑的运行优化记录完善的鼓风状态数据, 也难以及时发现鼓风管被窑内渣块击中等安全隐患.
针对上述问题, 本文提出一种基于关键点识别的鼓风管位置监测方法. 首先, 文章对从窑头看火口采集的火焰视频
数据集设计一种邻域关键点辅助的数据扩充方法, 并构建级联金字塔网络(CPN)来预测鼓风管管口中心点的位置;
然后, 本文提出一种基于多帧图像的聚类分析算法来消除因烟尘遮挡所产生的异常点, 并采用一种量化指标来实现
对挥发窑鼓风管摆放位置的实时感知与记录; 最后, 本文基于现场采集的火焰视频数据进行了对比实验, 结果表明
所提出的关键点检测模型精度高、鲁棒性强, 且鼓风管位置的量化准确率高达92.3%. |
| 英文摘要 |
| The position of blower pipe at the kiln head is one of the important operating parameters that affect the
combustion state of the zinc oxide volatilization kiln. Currently, it is still manually adjusted through fire observation. At
the same time, there is no blower state data recorded for the operation optimization of the volatilization kiln. It is also
difficult to find the hidden safety hazards in time, such as the blower pipe being hit by the slag in the kiln. To tackle the
above problems, a method for monitoring the position of blower pipe based on the keypoints identification is proposed.
Firstly, for the flame video dataset collected from the kiln head, a data augmentation method assisted by neighborhood
keypoints is designed, and the cascaded pyramid network (CPN) is constructed to predict the center position of blower
pipe nozzle. Then, a cluster analysis algorithm based on the multi-frame images is proposed to eliminate the outliers
caused by smoke and dust occlusion, and a quantitative index is used to realize the real-time perception and recording of
blower pipe position for the volatilization kiln. Finally, some comparative experiments are carried out based on the flame
video data. Experimental results show that the keypoints detection model has high accuracy and strong robustness, and the
quantification accuracy proposed is as high as 92.3%. |
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