Research on LiDAR Clear Air Turbulence Recognition Based on Improved SE-ResNet50
To address the issue of LiDAR’s low turbulence recognition rate at airports in low-altitude areas, a clear air turbulence recognition method based on an improved Squeeze-and-Excitation Residual Network with 50 layers (SE-ResNet50) is proposed. By introducing the squeeze-and-excitation module and imp...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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China Science Publishing & Media Ltd. (CSPM)
2025-06-01
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| Series: | Leida xuebao |
| Subjects: | |
| Online Access: | https://radars.ac.cn/cn/article/doi/10.12000/JR25042 |
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| _version_ | 1849685803070914560 |
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| author | Zibo ZHUANG Jun CHEN Peilin HE Hongying ZHANG Guohua JIN Xiong LUO |
| author_facet | Zibo ZHUANG Jun CHEN Peilin HE Hongying ZHANG Guohua JIN Xiong LUO |
| author_sort | Zibo ZHUANG |
| collection | DOAJ |
| description | To address the issue of LiDAR’s low turbulence recognition rate at airports in low-altitude areas, a clear air turbulence recognition method based on an improved Squeeze-and-Excitation Residual Network with 50 layers (SE-ResNet50) is proposed. By introducing the squeeze-and-excitation module and improving the network structure, the model’s excessive sensitivity to feature location is reduced, thereby enabling the network to selectively highlight useful information features during the learning process. A sample dataset was established using measured data from Lanzhou Zhongchuan International Airport; for model training, a balanced dataset was created by extracting an equal amount of weak, moderate, and strong turbulence data based on the turbulence classification level. Under the same experimental conditions, the recognition accuracy of the improved SE-ResNet50 was increased by 7.44%, 6.52%, and 4.11% compared with the convolutional neural network, MobileNetV2, and ShuffleNetV1 networks, respectively. A comparison of the confusion matrices generated by each model showed that the accuracy of the proposed method reached 95%, verifying the feasibility of the proposed method. |
| format | Article |
| id | doaj-art-0a480b25d4904d4dbcada04204994568 |
| institution | DOAJ |
| issn | 2095-283X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | China Science Publishing & Media Ltd. (CSPM) |
| record_format | Article |
| series | Leida xuebao |
| spelling | doaj-art-0a480b25d4904d4dbcada042049945682025-08-20T03:22:58ZengChina Science Publishing & Media Ltd. (CSPM)Leida xuebao2095-283X2025-06-0114362964010.12000/JR25042R25042Research on LiDAR Clear Air Turbulence Recognition Based on Improved SE-ResNet50Zibo ZHUANG0Jun CHEN1Peilin HE2Hongying ZHANG3Guohua JIN4Xiong LUO5Institute of Aviation Meteorology, Civil Aviation University of China, Tianjin 300300, ChinaCollege of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, ChinaCollege of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, ChinaCollege of Electronic Information and Automation, Civil Aviation University of China, Tianjin 300300, ChinaSouthwest Institute of Technical Physics, Chengdu 610041, ChinaSouthwest Institute of Technical Physics, Chengdu 610041, ChinaTo address the issue of LiDAR’s low turbulence recognition rate at airports in low-altitude areas, a clear air turbulence recognition method based on an improved Squeeze-and-Excitation Residual Network with 50 layers (SE-ResNet50) is proposed. By introducing the squeeze-and-excitation module and improving the network structure, the model’s excessive sensitivity to feature location is reduced, thereby enabling the network to selectively highlight useful information features during the learning process. A sample dataset was established using measured data from Lanzhou Zhongchuan International Airport; for model training, a balanced dataset was created by extracting an equal amount of weak, moderate, and strong turbulence data based on the turbulence classification level. Under the same experimental conditions, the recognition accuracy of the improved SE-ResNet50 was increased by 7.44%, 6.52%, and 4.11% compared with the convolutional neural network, MobileNetV2, and ShuffleNetV1 networks, respectively. A comparison of the confusion matrices generated by each model showed that the accuracy of the proposed method reached 95%, verifying the feasibility of the proposed method.https://radars.ac.cn/cn/article/doi/10.12000/JR25042light detection and ranging (lidar)eddy dissipation rate (edr)clear air turbulenceresidual network (resnet)deep learning |
| spellingShingle | Zibo ZHUANG Jun CHEN Peilin HE Hongying ZHANG Guohua JIN Xiong LUO Research on LiDAR Clear Air Turbulence Recognition Based on Improved SE-ResNet50 Leida xuebao light detection and ranging (lidar) eddy dissipation rate (edr) clear air turbulence residual network (resnet) deep learning |
| title | Research on LiDAR Clear Air Turbulence Recognition Based on Improved SE-ResNet50 |
| title_full | Research on LiDAR Clear Air Turbulence Recognition Based on Improved SE-ResNet50 |
| title_fullStr | Research on LiDAR Clear Air Turbulence Recognition Based on Improved SE-ResNet50 |
| title_full_unstemmed | Research on LiDAR Clear Air Turbulence Recognition Based on Improved SE-ResNet50 |
| title_short | Research on LiDAR Clear Air Turbulence Recognition Based on Improved SE-ResNet50 |
| title_sort | research on lidar clear air turbulence recognition based on improved se resnet50 |
| topic | light detection and ranging (lidar) eddy dissipation rate (edr) clear air turbulence residual network (resnet) deep learning |
| url | https://radars.ac.cn/cn/article/doi/10.12000/JR25042 |
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