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
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.
安健健, 孟庆岩, 胡蝶, 胡新礼, 杨健, 杨天梁. 基于Faster R-CNN的火电厂冷却塔检测及工作状态判定[J]. 国土资源遥感, 2021, 33(2): 93-99.
AN Jianjian, MENG Qingyan, HU Die, HU Xinli, YANG Jian, YANG Tianliang. The detection and determination of the working state of cooling tower in the thermal power plant based on Faster R-CNN. Remote Sensing for Land & Resources, 2021, 33(2): 93-99.
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2021, Vol. 33 
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