Exploration on automatic identification algorithm of transmission line mountain fire based on image recognition technology
In recent years, many places have experienced frequent mountain fires, which have become one of the main disasters in the operation of power transmission lines. However, traditional manual inspection and video monitoring methods can only detect a small amount of mountain fires, and require a large a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-11-01
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| Series: | International Journal of Thermofluids |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666202723002094 |
| _version_ | 1797401632538886144 |
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| author | Wei He Wenjun Chen Yulin Wang Yu Liu Shenghong Wang |
| author_facet | Wei He Wenjun Chen Yulin Wang Yu Liu Shenghong Wang |
| author_sort | Wei He |
| collection | DOAJ |
| description | In recent years, many places have experienced frequent mountain fires, which have become one of the main disasters in the operation of power transmission lines. However, traditional manual inspection and video monitoring methods can only detect a small amount of mountain fires, and require a large amount of manpower and material resources. In this paper, image recognition technology was used to study the automatic identification algorithm of transmission line mountain fires, and image recognition technology was used to denoise the extracted images. After that, feature extraction was performed on the successfully denoised image, and the image was enhanced to improve the clarity of the image and prepare for improving the recognition accuracy of mountain fires. Through experiments, it can be found that using image recognition technology to identify mountain fires not only has high accuracy and recognition speed, but also has a lower error rate compared to using satellite meteorological data. The recognition accuracy of image recognition technology was above 95 %, while the recognition accuracy of using satellite meteorological data was below 92 %. |
| first_indexed | 2024-03-09T02:13:52Z |
| format | Article |
| id | doaj.art-8e5ffdc475fa46e396ba88f6eade54f1 |
| institution | Directory Open Access Journal |
| issn | 2666-2027 |
| language | English |
| last_indexed | 2024-03-09T02:13:52Z |
| publishDate | 2023-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Thermofluids |
| spelling | doaj.art-8e5ffdc475fa46e396ba88f6eade54f12023-12-07T05:30:57ZengElsevierInternational Journal of Thermofluids2666-20272023-11-0120100494Exploration on automatic identification algorithm of transmission line mountain fire based on image recognition technologyWei He0Wenjun Chen1Yulin Wang2Yu Liu3Shenghong Wang4State Grid Guoluo Power Supply Company, Guoluo 814000, Qinghai, ChinaState Grid Guoluo Power Supply Company, Guoluo 814000, Qinghai, China; Corresponding author.Data Operation Center of Information and Communication Company of State Grid Qinghai Electric Power Company, Xining 810001, Qinghai, ChinaState Grid Guoluo Power Supply Company, Guoluo 814000, Qinghai, ChinaState Grid Guoluo Power Supply Company, Guoluo 814000, Qinghai, ChinaIn recent years, many places have experienced frequent mountain fires, which have become one of the main disasters in the operation of power transmission lines. However, traditional manual inspection and video monitoring methods can only detect a small amount of mountain fires, and require a large amount of manpower and material resources. In this paper, image recognition technology was used to study the automatic identification algorithm of transmission line mountain fires, and image recognition technology was used to denoise the extracted images. After that, feature extraction was performed on the successfully denoised image, and the image was enhanced to improve the clarity of the image and prepare for improving the recognition accuracy of mountain fires. Through experiments, it can be found that using image recognition technology to identify mountain fires not only has high accuracy and recognition speed, but also has a lower error rate compared to using satellite meteorological data. The recognition accuracy of image recognition technology was above 95 %, while the recognition accuracy of using satellite meteorological data was below 92 %.http://www.sciencedirect.com/science/article/pii/S2666202723002094Automatic recognition algorithmTransmission lineImage recognition technologyImage feature extractionSatellite meteorological data |
| spellingShingle | Wei He Wenjun Chen Yulin Wang Yu Liu Shenghong Wang Exploration on automatic identification algorithm of transmission line mountain fire based on image recognition technology International Journal of Thermofluids Automatic recognition algorithm Transmission line Image recognition technology Image feature extraction Satellite meteorological data |
| title | Exploration on automatic identification algorithm of transmission line mountain fire based on image recognition technology |
| title_full | Exploration on automatic identification algorithm of transmission line mountain fire based on image recognition technology |
| title_fullStr | Exploration on automatic identification algorithm of transmission line mountain fire based on image recognition technology |
| title_full_unstemmed | Exploration on automatic identification algorithm of transmission line mountain fire based on image recognition technology |
| title_short | Exploration on automatic identification algorithm of transmission line mountain fire based on image recognition technology |
| title_sort | exploration on automatic identification algorithm of transmission line mountain fire based on image recognition technology |
| topic | Automatic recognition algorithm Transmission line Image recognition technology Image feature extraction Satellite meteorological data |
| url | http://www.sciencedirect.com/science/article/pii/S2666202723002094 |
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