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Convolutional neural networks based on multi-scale additive merging layers for visual smoke recognition

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Abstract

Traditional smoke recognition methods are mainly based on handcrafted features. However, it is difficult to design handcrafted features that are robust and discriminative for smoke recognition because of large variations in smoke color, shapes and textures. To solve this problem, we specifically design a basic block of convolutional neural networks (CNNs) and stack basic blocks to propose a novel deep multi-scale CNN (DMCNN) for smoke recognition. The basic block consists of several parallel convolutional layers with the same number of filters but different kernel sizes for scale invariance. Each convolutional layer is followed by a batch normalization to normalize the output of the convolutional layer. Then the basic block sums up all normalized outputs from multi-scale parallel layers and activates the sum as the final output of the block. To fully extract scale invariant features, we cascade eleven basic blocks, which is followed by a global average pooling and a 2D fully connected layer, to construct DMCNN. Experimental results show that our method achieves higher detection rates, higher accuracy rates and lower false alarm rates than existing methods. To further verify the efficiency of DMCNN, we also conducted face gender recognition experiments on the LFW database and our model also achieves obviously higher accuracy rates than other methods. Furthermore, our method is an efficient, lightweight CNN model with about 1 M parameters that are far less than other CNN methods.

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Acknowledgements

This work was partially supported by Natural Science Foundation of China (61862029), Science Technology Application Project of Jiangxi Province (KJLD12066) and Science Technology Projects of Jiangxi Province (GJJ170317).

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Authors and Affiliations

  1. School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, 330032, Jiangxi, China

    Feiniu Yuan, Lin Zhang, Boyang Wan & Xue Xia

  2. College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai, 201418, China

    Feiniu Yuan

  3. School of Mathematics and Computer Science, Jiangxi Science and Technology Normal University, Nanchang, 330045, Jiangxi, China

    Lin Zhang

  4. Vocational School of Teachers and Technology, Jiangxi Agricultural University, Nanchang, 330045, Jiangxi, China

    Jinting Shi

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  1. Feiniu Yuan
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Correspondence to Lin Zhang.

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Yuan, F., Zhang, L., Wan, B. et al. Convolutional neural networks based on multi-scale additive merging layers for visual smoke recognition. Machine Vision and Applications 30, 345–358 (2019). https://doi.org/10.1007/s00138-018-0990-3

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  • DOI: https://doi.org/10.1007/s00138-018-0990-3

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