A Lightning Optical Automatic Detection Method Based on a Deep Neural Network
To achieve automatic recognition of lightning images, which cannot easily be handled using the existing methods and still requires significant human resources, we propose a lightning image dataset and a preprocessing method. The lightning image data over five months were collected using a camera bas...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/16/7/1151 |
| _version_ | 1797212001053704192 |
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| author | Jialei Wang Lin Song Qilin Zhang Jie Li Quanbo Ge Shengye Yan Gaofeng Wu Jing Yang Yuqing Zhong Qingda Li |
| author_facet | Jialei Wang Lin Song Qilin Zhang Jie Li Quanbo Ge Shengye Yan Gaofeng Wu Jing Yang Yuqing Zhong Qingda Li |
| author_sort | Jialei Wang |
| collection | DOAJ |
| description | To achieve automatic recognition of lightning images, which cannot easily be handled using the existing methods and still requires significant human resources, we propose a lightning image dataset and a preprocessing method. The lightning image data over five months were collected using a camera based on two optical observation stations, and then a series of batch labeling methods were applied, which greatly reduced the workload of subsequent manual labeling, and a dataset containing more than 30,000 labeled samples was established. Considering that lightning varies rapidly over time, we propose a time sequence composite (TSC) preprocessing method that inputs lightning’s time-varying characteristics into a model for better recognition of lightning images. The TSC method was evaluated through an experiment on four backbones, and it was found that this preprocessing method enhances the classification performance by 40%. The final trained model could successfully distinguish between the “lightning” and “non-lightning” samples, and a recall rate of 86.5% and a false detection rate of 0.2% were achieved. |
| first_indexed | 2024-04-24T10:35:26Z |
| format | Article |
| id | doaj.art-3c813e32acca4fe6b54355cf5a1c8cf3 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-24T10:35:26Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-3c813e32acca4fe6b54355cf5a1c8cf32024-04-12T13:25:27ZengMDPI AGRemote Sensing2072-42922024-03-01167115110.3390/rs16071151A Lightning Optical Automatic Detection Method Based on a Deep Neural NetworkJialei Wang0Lin Song1Qilin Zhang2Jie Li3Quanbo Ge4Shengye Yan5Gaofeng Wu6Jing Yang7Yuqing Zhong8Qingda Li9Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Research Institute of Intelligent-Sensing and Disaster Prevention for Extreme Weather/Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, ChinaQingdao Ecological and Agricultural Meteorological Center, Qingdao 266003, ChinaCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Research Institute of Intelligent-Sensing and Disaster Prevention for Extreme Weather/Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, ChinaCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Research Institute of Intelligent-Sensing and Disaster Prevention for Extreme Weather/Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, ChinaCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Research Institute of Intelligent-Sensing and Disaster Prevention for Extreme Weather/Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, ChinaCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Research Institute of Intelligent-Sensing and Disaster Prevention for Extreme Weather/Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, ChinaCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Research Institute of Intelligent-Sensing and Disaster Prevention for Extreme Weather/Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, ChinaCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Research Institute of Intelligent-Sensing and Disaster Prevention for Extreme Weather/Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, ChinaCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Research Institute of Intelligent-Sensing and Disaster Prevention for Extreme Weather/Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, ChinaCollaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)/Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)/Research Institute of Intelligent-Sensing and Disaster Prevention for Extreme Weather/Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, ChinaTo achieve automatic recognition of lightning images, which cannot easily be handled using the existing methods and still requires significant human resources, we propose a lightning image dataset and a preprocessing method. The lightning image data over five months were collected using a camera based on two optical observation stations, and then a series of batch labeling methods were applied, which greatly reduced the workload of subsequent manual labeling, and a dataset containing more than 30,000 labeled samples was established. Considering that lightning varies rapidly over time, we propose a time sequence composite (TSC) preprocessing method that inputs lightning’s time-varying characteristics into a model for better recognition of lightning images. The TSC method was evaluated through an experiment on four backbones, and it was found that this preprocessing method enhances the classification performance by 40%. The final trained model could successfully distinguish between the “lightning” and “non-lightning” samples, and a recall rate of 86.5% and a false detection rate of 0.2% were achieved.https://www.mdpi.com/2072-4292/16/7/1151lightning detectionautomatic detectiondeep neural networkimage recognition |
| spellingShingle | Jialei Wang Lin Song Qilin Zhang Jie Li Quanbo Ge Shengye Yan Gaofeng Wu Jing Yang Yuqing Zhong Qingda Li A Lightning Optical Automatic Detection Method Based on a Deep Neural Network Remote Sensing lightning detection automatic detection deep neural network image recognition |
| title | A Lightning Optical Automatic Detection Method Based on a Deep Neural Network |
| title_full | A Lightning Optical Automatic Detection Method Based on a Deep Neural Network |
| title_fullStr | A Lightning Optical Automatic Detection Method Based on a Deep Neural Network |
| title_full_unstemmed | A Lightning Optical Automatic Detection Method Based on a Deep Neural Network |
| title_short | A Lightning Optical Automatic Detection Method Based on a Deep Neural Network |
| title_sort | lightning optical automatic detection method based on a deep neural network |
| topic | lightning detection automatic detection deep neural network image recognition |
| url | https://www.mdpi.com/2072-4292/16/7/1151 |
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