Wildland Forest Fire Smoke Detection Based on Faster R-CNN using Synthetic Smoke Images
Qixing ZhangState Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, ChinaGaohua LinState Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, ChinaYongming ZhangState Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, ChinaXu GaoState Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, ChinaJinjun WangState Key Laboratory of Fire Science, University of Science and Technology of China, Hefei 230026, China
2018en
ABI
Annotatsiya
In this paper, Faster R-CNN was used to detect wildland forest fire smoke to avoid the complex manually feature extraction process in traditional video smoke detection methods. Synthetic smoke images are produced by inserting real smoke or simulative smoke into forest background to solve the lack of training data. The models trained by the two kinds of synthetic images respectively are tested in dataset consisting of real fire smoke images. The results show that simulative smoke is the better choice and the model is insensitive to thin smoke. It may be possible to further boost the performance by improving the synthetic process of forest fire smoke images or extending this solution to video sequences.
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