A video-based SlowFastMTB model for detection of small amounts of smoke from incipient forest fires. Issue 2 (19th April 2022)

• Record Type: Journal Article
• Authors: Choi, Minseok; Kim, Chungeon; Oh, Hyunseok
• Is Part Of: Journal of computational design and engineering. Volume 9:Issue 2(2022)
• Page Start: 793
• Page End: 804
• Publication Date: 2022-04-19
• Subjects: forest fire -- smoke detection -- deep learning -- early detection -- annotation; Engineering -- Data processing -- Periodicals; Computer-aided design -- Periodicals; Computer-aided design; Engineering -- Data processing; Electronic journals; Electronic journals; Periodicals; 620.0042
• DOI: 10.1093/jcde/qwac027
• Languages: English
• ISSNs: 2288-4300

Abstract

Abstract: This paper proposes a video-based SlowFast model that combines the SlowFast deep learning model with a new boundary box annotation algorithm. The new algorithm, namely the MTB (i.e., the ratio of the number of Moving object pixels To the number of Bounding box pixels) algorithm, is devised to automatically annotate the bounding box that includes the smoke with fuzzy boundaries. The model parameters of the MTB algorithm are examined by multifactor analysis of variance. To demonstrate the validity of the proposed approach, a case study is provided that examines real video clips of incipient forest fires with small amounts of smoke. The performance of the proposed approach is compared with those of existing deep learning models, including convolutional neural network (CNN), faster region-based CNN (faster R-CNN), and SlowFast. It is demonstrated that the proposed approach achieves enhanced detection accuracy, while reducing false negative rates. Graphical Abstract: