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A dataset for fire and smoke object detection

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Abstract

Fire and smoke object detection is of great significance due to the extreme destructive power of fire disasters. Most of the existing methods, whether traditional computer vision-based models with sensors or deep learning-based models have circumscribed application scenes with relatively poor detection speed and accuracy. This means seldom taking smoke into consideration and always focusing on classification tasks. To advance object detection research in fire and smoke detection, we introduce a dataset called DFS (Dataset for Fire and Smoke detection), which is of high quality, constructed by collecting from real scenes and annotated by strict and reasonable rules. To reduce the possibility of erroneous judgments caused by objects that are similar to fires in color and brightness, apart from annotating ‘fire’ and ‘smoke’, we annotate these objects as a new class ‘other’. There are a total of 9462 images named by the fire size, which can benefit different detection tasks. Furthermore, by carrying out extensive and abundant experiments on Various object detection models, we provide a comprehensive benchmark on our dataset. Experimental results show that DFS well represents real applications in fire and smoke detection and is quite challenging. We also test models with different training and testing proportions on our dataset to find the optimal split ratio in real situations. The dataset is released at https://github.com/siyuanwu/DFS-FIRE-SMOKE-Dataset.

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Acknowledgements

Siyuan Wu and Xinrong Zhang are co-first authors of the article.

Funding

No funding was received to assist with the preparation of this manuscript.

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Authors and Affiliations

  1. College of Computer Science and Engineering, Xi’an University of Technology, Xi’an, 710048, Shaanxi, China

    Siyuan Wu

  2. Key Laboratory of Spectral Imaging Technology CAS, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, 710119, Shaanxi, China

    Siyuan Wu & Ruqi Liu

  3. Tandon School of Engineering, New York University, New York, NY, 10003, USA

    Xinrong Zhang

  4. University of Chinese Academy of Sciences, Beijing, 100049, China

    Ruqi Liu

  5. Shaanxi Avition Engineering Company Limited, Xi’an, 710121, Shaanxi, China

    Binhai Li

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  1. Siyuan Wu
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  4. Binhai Li
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Correspondence to Siyuan Wu.

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Wu, S., Zhang, X., Liu, R. et al. A dataset for fire and smoke object detection. Multimed Tools Appl 82, 6707–6726 (2023). https://doi.org/10.1007/s11042-022-13580-x

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