Three-dimensional-imaging thermal surfaces of coal fires based on UAV thermal infrared data

Shao, Zhenlu; Li, Yizhou; Deng, Rong; Zhong, Xiaoxing

doi:10.1080/01431161.2020.1823044

Cited by 14 publications

(7 citation statements)

References 31 publications

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“…Regarding the spontaneous combustion of coal waste dumps, previous studies focused on the extraction and three‐dimensional modeling of surface temperature. Previously point‐to‐point monitoring using temperature probes using thermal infrared imaging combined with GPS (Y. Wang et al, 2008), close‐range photogrammetry technology (Hu & Xia, 2017), satellite (Nádudvari, 2014), and UAV thermal infrared remote sensing technology (Shao et al, 2021; Yuan et al, 2021) achieved spatial monitoring and significantly improved the accuracy and speed of temperature acquisition. However, its application scenarios were mostly used in coal waste dumps with a large intensity and range of spontaneous combustion, and without good ecological restoration.…”

Section: Discussionmentioning

confidence: 99%

Monitoring potential spontaneous combustion in a coal waste dump after reclamation through unmanned aerial vehicle RGB imagery based on alfalfa aboveground biomass

et al. 2022

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Section: Discussionmentioning

confidence: 99%

Monitoring potential spontaneous combustion in a coal waste dump after reclamation through unmanned aerial vehicle RGB imagery based on alfalfa aboveground biomass

et al. 2022

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“…Согласно результатам РФА его химический со став следующий (%): Sобщая -3,99; SiO 2 -58,75; al 2 O 3 -15,38; fe 2 O 3общее -5,48; MgO -3,58; K 2 O -0,95; tiO 2 -0,26; CaO -6,7; N 2 O -0,18; P 2 O 5 -0,03; MnO -0,03. Наряду с этим он содержит (ppm): Ва (300), f (300), Ce (79), Cu (74), Zn (60), Nd (51), la (48), Pb (34), ga (27), Cr (24), Ni (15), as (11) и V (5).…”

Section: рис 2 открытый тип пирогенеза на терриконе (а) который спосо...unclassified

“…Вероятно, они контролируют началь ную стадию природного пиролиза, пока не происхо дит самовозгорание продуктов пиролиза. В последние годы многие исследователи посвятили свои работы их изучению и открыли совершенно новую страницу в по знании минералогии техногенеза и пирометаморфизма [9,10,11,12,13,14,15,16].…”

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Pyrogenesis of Coal-Bearing Deposits of Waste Heaps Kuznetsk Basin

Akulova¹,

Prokopiev²,

Alekseeva³

2022

Ugol'

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ГОРНАя ТЕПлОфИЗИКАВ статье приведены результаты исследования процессов горения техногенно-переотложенных угленосных отложений на юге Кузнецкого бассейна (Осинниковское месторождение каменных углей). На основе литолого-геохимических данных пирогенно измененных угленосных отложений терриконов выявлено несколько закономерностей. Горение террикона представляет собой медленно развивающийся поступательный процесс, который определяет формирование трех зон: красноцветных горелых пород; активного горения (пирогенеза) и черноцветных продуктов пиролиза. Терриколиты -особый новообразованный продукт, сформированный в процессе пирогенеза угленосных отложений в приповерхностной части террикона, в котором содержание кремнезема достигает 95%.

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“…Finally, the type of the implemented integrated circuits (ICs) were Field-Programmable Gate Array (FPGA). GNSS [47] Bluetooth [19] On-board Computers [20,30,48] Concerning the communication and monitoring purposes, Bluetooth modules, smartphones and tablets were used. As regards the navigation and positioning systems, the main module was GPS.…”

Section: Camerasmentioning

confidence: 99%

Computer Vision for Fire Detection on UAVs—From Software to Hardware

2021

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Fire hazard is a condition that has potentially catastrophic consequences. Artificial intelligence, through Computer Vision, in combination with UAVs has assisted dramatically to identify this risk and avoid it in a timely manner. This work is a literature review on UAVs using Computer Vision in order to detect fire. The research was conducted for the last decade in order to record the types of UAVs, the hardware and software used and the proposed datasets. The scientific research was executed through the Scopus database. The research showed that multi-copters were the most common type of vehicle and that the combination of RGB with a thermal camera was part of most applications. In addition, the trend in the use of Convolutional Neural Networks (CNNs) is increasing. In the last decade, many applications and a wide variety of hardware and methods have been implemented and studied. Many efforts have been made to effectively avoid the risk of fire. The fact that state-of-the-art methodologies continue to be researched, leads to the conclusion that the need for a more effective solution continues to arouse interest.

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Three-dimensional-imaging thermal surfaces of coal fires based on UAV thermal infrared data

Cited by 14 publications

References 31 publications

Monitoring potential spontaneous combustion in a coal waste dump after reclamation through unmanned aerial vehicle RGB imagery based on alfalfa aboveground biomass

Monitoring potential spontaneous combustion in a coal waste dump after reclamation through unmanned aerial vehicle RGB imagery based on alfalfa aboveground biomass

Pyrogenesis of Coal-Bearing Deposits of Waste Heaps Kuznetsk Basin

Computer Vision for Fire Detection on UAVs—From Software to Hardware

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