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Detonator coded character spotting based on convolutional neural networks

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Abstract

To facilitate the management of detonators, an automatic spotting system is proposed for detonator coded characters based on convolutional neural networks. The system contains a multi-scale detection network, a multi-label recognition network and a post-processing layer. An improved fully convolution network (FCN) is designed as the multi-scale detection network to obtain the accurate response map of the detonator image. Two subnetworks are parallel integrated into the FCN to perform a coarse-to-fine detection. An improved Jaccard loss function with a regularization term is defined to train the FCN. The region of interest (ROI) of the detonator image is achieved when the response map is post-processed by the post-processing layer. Finally, a modified multi-label network is used to recognize the detonator coded characters in the ROI. The experimental results indicate that the proposed system achieves a better spotting performance for detonator coded characters than the state-of-the-art text spotting methods in terms of accuracy and efficiency.

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Acknowledgements

This work was in part supported by the National Natural Science Foundation of China (Grant No. 61001179) and the Key Project of Industry-University-Research Collaborative Innovation in Guangzhou, China (No. 201802020010).

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Authors and Affiliations

  1. School of Information Engineering, Guangdong University of Technology, Guangzhou, China

    Guandong Cen, Nian Cai, Jixiu Wu & Feiyang Li

  2. School of Electromechanical Engineering, Guangdong University of Technology, Guangzhou, China

    Han Wang

  3. Guandong Face Intelligent Technology Co. Ltd, Guangzhou, China

    Guotian Wang

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  1. Guandong Cen
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  2. Nian Cai
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  3. Jixiu Wu
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  4. Feiyang Li
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  5. Han Wang
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  6. Guotian Wang
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Correspondence to Nian Cai.

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Cen, G., Cai, N., Wu, J. et al. Detonator coded character spotting based on convolutional neural networks. SIViP 14, 67–75 (2020). https://doi.org/10.1007/s11760-019-01525-1

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