The Research of Real-Time Forest Fire Alarm Algorithm Based on Video

Song, Lei; Wang, Bo; Wang, Hailuo; Wu, Shujie

doi:10.1109/ihmsc.2014.34

Cited by 4 publications

(1 citation statement)

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“…Fine particles such as air dust might cause interference to the detector, resulting in a limited monitoring range and low accuracy. Compared with smoke detectors, the image-based smoke recognition method has the advantages of wide monitoring range and low cost, and is widely used in the field of forest fire monitoring (Song et al 2014). This image-based method designs an image processing algorithm to detect smoke motion blocks from video stream data.…”

Section: Introductionmentioning

confidence: 99%

FireFormer: an efficient Transformer to identify forest fire from surveillance cameras

Qiao

Wang

et al. 2023

Int. J. Wildland Fire

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Background An effective identification model is crucial to realise the real-time monitoring and early warning of forest fires from surveillance cameras. However, existing models are prone to generate numerous false alarms under the interference of artificial smoke such as industrial smoke and villager cooking smoke, therefore a superior identification model is urgently needed. Aims In this study, we tested the Transformer-based model FireFormer to predict the risk probability of forest fire from the surveillance images. Methods FireFormer uses a shifted window self-attention module to extract similarities of divided patches in the image. The similarity in characteristics indicated the probability of forest fires. The GradCAM algorithm was then applied to analyse the interest area of FireFormer model and visualise the contribution of different image patches by calculating gradient reversely. To verify our model, the monitoring data from the high-point camera in Nandan Mountain, Foshan City, was collected and further constructed as a forest fire alarm dataset. Key results Our results showed that FireFormer achieved a competitive performance (OA: 82.21%, Recall: 86.635% and F1-score: 74.68%). Conclusions FireFormer proves to be superior to traditional methods. Implications FireFormer provides an efficient way to reduce false alarms and avoid heavy manual re-checking work.

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Section: Introductionmentioning

confidence: 99%