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Remote Sensing for Land & Resources    2021, Vol. 33 Issue (2) : 93-99     DOI: 10.6046/gtzyyg.2020184
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The detection and determination of the working state of cooling tower in the thermal power plant based on Faster R-CNN
AN Jianjian1,2(), MENG Qingyan2,3,4(), HU Die2,3, HU Xinli2,3, YANG Jian2,3, YANG Tianliang3,4
1. Faculty of Land Resources Engineering, Kunming University of Science and Technology, Kunming 650093, China
2. Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
3. Sanya Institute of Remote Sensing, Sanya 572029, China
4. Hainan Research Institute, Aerospace Information Research Institute, Chinese Academy of Sciences, Sanya 572029, China
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Abstract  

Pollutants from thermal power plants are discharged into the air, posing a great threat to urban ecological environments and resident health. However, currently, there is no effective method for power plant detection and working state determination. This paper proposes a thermal power plant cooling tower detection method with cooling towers as the key target and then further captures the production state information from water vapor discharge from the top of cooling towers based on the Faster R-CNN deep learning network. 8 typical thermal power plants are provided to verify the proposed method. The ideal results have been achieved in these test scenarios, which implies that this method can be effectively applied to the working state detection of thermal power plants. In addition, the applicability of this method can be broadened to similar industrial targets, such as steel mills and nuclear power plants. Creatively applying a deep learning network to determining the target working state is the authors' theoretical contribution that develops an innovative orientation, and this method could provide practical guidance for the governance of urban industrial gas emissions.
Keywords deep learning      Faster R-CNN      target detection      cooling tower      working state     
ZTFLH:  TP751P407.8  
Corresponding Authors: MENG Qingyan     E-mail: awckmust@163.com;mengqy@radi.ac.cn
Issue Date: 21 July 2021
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Jianjian AN
Qingyan MENG
Die HU
Xinli HU
Jian YANG
Tianliang YANG
Cite this article:   
Jianjian AN,Qingyan MENG,Die HU, et al. The detection and determination of the working state of cooling tower in the thermal power plant based on Faster R-CNN[J]. Remote Sensing for Land & Resources, 2021, 33(2): 93-99.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2020184     OR     https://www.gtzyyg.com/EN/Y2021/V33/I2/93
Fig.1  Cooling tower structure
Fig.2  Faster R-CNN target detection network structure
Fig.3  Partial structure diagram of VGG16
Fig.4  Annotation example
Fig.5  Data augmentation
改变亮度 旋转 CutMix Mosaic mAP/%
√ 88.10
√ 90.83
√ 89.76
√ 93.30
√ √ √ 93.69
√ √ √ √ 94.88
Tab.1  Experimental results of different data augmentation technologies
Fig.6  Training loss value change
Fig.7  Precision-recall curves of different feature extraction networks
Fig.8  Detection results of different feature extraction networks
Fig.9  Power plant detection results with different algorithms
模型 AP mAP
powerstation_w powerstation_nw
SSD 87.52 73.76 80.64
YOLOv3 93.20 94.38 93.79
Faster R-CNN(VGG16) 90.86 98.90 94.88
Tab.2  Comparative analysis of three different algorithms(%)
Fig.10  Power plant cooling tower detection results
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