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Ship recognition method combined with image segmentation and deep learning feature extraction in video surveillance

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Abstract

To solve the problem of ship recognition in video images, a ship recognition method based on Morphological Watershed image segmentation and Zemike moment is proposed. Firstly, the video frame image is pre-processed by gray algorithm, and then the gray image is filtered by wavelet transform to remove noise. After denoising, the Morphological Watershed algorithm is used to segment the image and extract the ship area in the image. Next, the feature of ship image is extracted based on deep learning convolution neural network (CNN) and Zemike moment method. Finally, the KNN-SVM classifier is trained according to the image features and class labels to realize the automatic recognition of ships. Experimental results show that the method can effectively identify 3 types of ships, with an average detection accuracy of 87%.

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Acknowledgements

The work was partially supported by National Natural Science Foundation of China (Grant No. 51479155), Natural Science Foundation of Hubei Province of China (Grant No.2014CFB190), Science and Technology Department of Bijie City of China (Grant No.29[2014]), Education and Teaching Reform Project of China University of Labor Relations [Grant No. JG1739].

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Authors and Affiliations

  1. School of Computer Science and Technology, Wuhan University of Technology, Wuhan, 430000, China

    Xiufeng Cao, Shu Gao & Liangchen Chen

  2. Hubei Key Laboratory of Transportation Internet of Things, Wuhan University of Technology, Wuhan, 430000, China

    Shu Gao

  3. Department of Computer Application, China Institute of Industrial Relations, Beijing, 100000, China

    Liangchen Chen

  4. School of Information Engineering, Guizhou University of Engineering Science, Bijie, 551700, China

    Yan Wang

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  1. Xiufeng Cao
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  2. Shu Gao
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  4. Yan Wang
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Correspondence to Xiufeng Cao.

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Cao, X., Gao, S., Chen, L. et al. Ship recognition method combined with image segmentation and deep learning feature extraction in video surveillance. Multimed Tools Appl 79, 9177–9192 (2020). https://doi.org/10.1007/s11042-018-7138-3

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  • DOI: https://doi.org/10.1007/s11042-018-7138-3

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