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ECS-STPM: An Efficient Model for Tunnel Fire Anomaly Detection

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Web and Big Data (APWeb-WAIM 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14334))

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Abstract

The fire spreads rapidly in the tunnel due to the narrow space and high sealing, which makes rescue hard and threatens the citizen’s lives. However, the lack of public fire datasets makes it challenging for networks to learn targeted representations of fire features, resulting in low detection accuracy. To tackle this problem, we construct a Tunnel Fire Anomaly Detection (TF-AD) dataset based on unsupervised training. This dataset contains 5200 high-resolution color images, including non-fire images for training and fire images with annotations for testing. Based on the TF-AD dataset, we propose an efficient tunnel fire anomaly detection model named ECS-STPM. ECS-STPM consists of a teacher and student network with identical EfficientNet-B1 structures. Additionally, considering the efficiency of adaptively assigning channel weights, we combine the convolutional kernel with channels to propose a novel attention mechanism, Efficient Kernel and Channel Attention (EKCA). EKCA replaces the Squeeze-and-Excitation (SE) networks in the MBConv module to prevent the loss of crucial information. Furthermore, we introduce the SPD-Conv module instead of the strided convolution layer to increase the detection accuracy in smaller fire areas. The experimental results on TF-AD dataset show that the pixel-level AUC-ROC and image-level AUC-ROC are up to 0.931 and 0.835, which verifies the effectiveness of our model.

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Acknowledgement

This work was supported in part by the National Natural Fund Joint Fund Project of China under Grant U21B2041.

Author information

Authors and Affiliations

  1. Chang’an University, Xi’an, China

    Huansheng Song, Ya Wen & ShiJie Sun

  2. Swinburne University of Technology, Melbourne, Australia

    Xiangyu Song

  3. Macquarie University, Sydney, Australia

    Taotao Cai

  4. Deakin University, Victoria, Australia

    Jianxin Li

Authors
  1. Huansheng Song
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  2. Ya Wen
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  3. Xiangyu Song
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  4. ShiJie Sun
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  5. Taotao Cai
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  6. Jianxin Li
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Corresponding author

Correspondence to Xiangyu Song .

Editor information

Editors and Affiliations

  1. Peng Cheng Laboratory, Shenzhen, China

    Xiangyu Song

  2. China University of Geosciences, Wuhan, China

    Ruyi Feng

  3. China University of Geosciences, Wuhan, China

    Yunliang Chen

  4. Deakin University, Burwood, VIC, Australia

    Jianxin Li

  5. University of Exeter, Exeter, UK

    Geyong Min

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Song, H., Wen, Y., Song, X., Sun, S., Cai, T., Li, J. (2024). ECS-STPM: An Efficient Model for Tunnel Fire Anomaly Detection. In: Song, X., Feng, R., Chen, Y., Li, J., Min, G. (eds) Web and Big Data. APWeb-WAIM 2023. Lecture Notes in Computer Science, vol 14334. Springer, Singapore. https://doi.org/10.1007/978-981-97-2421-5_19

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  • DOI: https://doi.org/10.1007/978-981-97-2421-5_19

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-97-2420-8

  • Online ISBN: 978-981-97-2421-5

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