Fully Synthetic Videos and the Random-Background-Pasting Method for Flame Segmentation Article Swipe
Yang Jia
,
Zhenbin Mao
,
Xinmeng Zhang
,
Yaxi Kuang
,
Yanping Chen
,
Qixing Zhang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/electronics12112492
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/electronics12112492
Video-based flame detection (VFD) aims to recognize fire events by using image features. Flame segmentation is an essential task in VFD, providing suspected regions for feature analysis and object recognition. However, the lack of positive flame samples makes it difficult to train deep-learning-based VFD models effectively. In this paper, we propose the assumption that we can train a segmentation model with virtual flame images and design experiments to prove it. We collected many virtual flame videos to extend existing flame datasets, which provide adequate flame samples for deep-learning-based VFD methods. We also apply a random-background-pasting method to distribute the flame images among different scenarios. The proposed method trains a flame segmentation model with zero real flame images. Moreover, we perform segmentation testing using real flame images, which the model has never used, to see if the model trained using ‘fake’ images can segment real objects. We trained four segmentation models based on FCN, U-Net, Deeplabv3, and Mask-RCNN using synthetic flame video frames and obtained the highest mPA of 0.783 and mIoU of 0.515. The experimental results on the FIRE-SMOKE-DATASET and the Fire-Detection-Image-Dataset demonstrate that the ‘fake’ flame samples generated by the proposed random-background-pasting method can obviously improve the performance of existing state-of-the-art flame segmentation methods using cross-dataset evaluation settings.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/electronics12112492
- https://www.mdpi.com/2079-9292/12/11/2492/pdf?version=1686041613
- OA Status
- gold
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4379055032
All OpenAlex metadata
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- OpenAlex ID
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https://openalex.org/W4379055032Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/electronics12112492Digital Object Identifier
- Title
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Fully Synthetic Videos and the Random-Background-Pasting Method for Flame SegmentationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
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2023-05-31Full publication date if available
- Authors
-
Yang Jia, Zhenbin Mao, Xinmeng Zhang, Yaxi Kuang, Yanping Chen, Qixing ZhangList of authors in order
- Landing page
-
https://doi.org/10.3390/electronics12112492Publisher landing page
- PDF URL
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https://www.mdpi.com/2079-9292/12/11/2492/pdf?version=1686041613Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2079-9292/12/11/2492/pdf?version=1686041613Direct OA link when available
- Concepts
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Segmentation, Artificial intelligence, Computer science, Feature (linguistics), Image segmentation, Pattern recognition (psychology), Computer vision, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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25Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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