Fire Detection Algorithm Combined with Image Processing and Flame Emission Spectroscopy

Qiu, Xuanbing; Xi, Tingyu; Sun, Dongyuan; Zhang, Enhua; Li, Chuanliang; Peng, Ying; Wei, Jilin; Wang, Gao

doi:10.1007/s10694-018-0727-x

Cited by 18 publications

(2 citation statements)

References 13 publications

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“…The results show that the simulation algorithm of forest fire spread geographic cellular automata is short in time; compared with the real fire data of Landsat Thematic Mapper images, the model has a high temporal and spatial consistency; the average Kappa coefficient is 0.6352 and the average accuracy is 87.89%, which can be used to simulate and predict the spread of forest fires [11]. Qiu et al (2018) studied a novel flame recognition algorithm in combustion process based on free radical emission spectroscopy, whi-ch=extracted multiple features from video images and processed the features through time smoothing algorithm to eliminate the false recognition rate. In the time smoothing experiment, the true positive rates of butane flame and forest fire were 0.965 and 0.937, respectively.…”

Section: Introductionmentioning

confidence: 98%

“…In the time smoothing experiment, the true positive rates of butane flame and forest fire were 0.965 and 0.937, respectively. Experimental results show that the algorithm can accurately identify real fires and determine combustion temperature by CH emission spectrum [12]. Trinh et al (2018) investigated the potential of MEC to address energy-management related applications on power-constrained limited IoT devices, while also providing low latency processing for visual data generation at high resolution.…”

Section: Introductionmentioning

confidence: 99%

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Adoption of image surface parameters under moving edge computing in the construction of mountain fire warning method

Cheng¹,

Zhou²,

Chai³

et al. 2020

PLoS ONE

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In order to cope with the problems of high frequency and multiple causes of mountain fires, it is very important to adopt appropriate technologies to monitor and warn mountain fires through a few surface parameters. At the same time, the existing mobile terminal equipment is insufficient in image processing and storage capacity, and the energy consumption is high in the data transmission process, which requires calculation unloading. For this circumstance, first, a hierarchical discriminant analysis algorithm based on image feature extraction is introduced, and the image acquisition software in the mobile edge computing environment in the android system is designed and installed. Based on the remote sensing data, the land surface parameters of mountain fire are obtained, and the application of image recognition optimization algorithm in the mobile edge computing (MEC) environment is realized to solve the problem of transmission delay caused by traditional mobile cloud computing (MCC). Then, according to the forest fire sensitivity index, a forest fire early warning model based on MEC is designed. Finally, the image recognition response time and bandwidth consumption of the algorithm are studied, and the occurrence probability of mountain fire in Muli county, Liangshan prefecture, Sichuan is predicted. The results show that, compared with the MCC architecture, the algorithm presented in this study has shorter recognition and response time to different images in WiFi network environment; compared with MCC, MEC architecture can identify close users and transmit less data, which can effectively reduce the bandwidth pressure of the network. In most areas of Muli county, Liangshan prefecture, the probability of mountain fire is relatively low, the probability of mountain fire caused by non-surface environment is about 8 times that of the surface environment, and the influence of non-surface environment in the period of high incidence of mountain fire is lower than that in the period of low incidence. In conclusion, the surface parameters of MEC can be used to effectively predict the mountain fire and provide preventive measures in time.

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Section: Introductionmentioning

confidence: 98%

Section: Introductionmentioning

confidence: 99%