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Ship Detection from Highly Cluttered Images Using Convolutional Neural Network

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Abstract

Classification and Detection of vessels from cluttered images is an actively researched field of computer vision. The complexity of the coastal area makes it more complex in distinguishing different vessels. Environment conditions and visual aspect of the vessels make classification problem more difficult. The visual aspect ratio is very small and similar to most type of vessels. Since the traditional CNNs methods are slow and not much accurate, we present the robust method for the classification of ships derived from Support Vector Machine, bag of features and Convolutional Neural Networks (CNNs). First of all Support Vector Machine which is a supervised transfer leaning technique is introduced to remove the false candidates. As the ships have wide range of variability, bag of features are applied to handle the diversified features of different categories of vessels. Finally CNNs framework is applied for the deep feature extraction to successfully classify the ships from database. The proposed algorithm is trained with 16 different categories of ships with a data set of more than 2700 images. The algorithm trains and validates the results in terms of confusion matrix and provides better accuracy to classify the ships. The proposed Convolutional Neural Networks (CNNs) model outperforms the other previous methods and gives an accuracy of 91.04 percentages.

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Authors and Affiliations

  1. University Institute of Engineering and Technology, Kurukshetra University Kurukshetra, Kurukshetra, India

    Vishal Gupta

  2. University Institute of Engineering and Technology, Kurukshetra University, Kurukshetra, India

    Monish Gupta

  3. Chandigarh Engineering College, Landran, Mohali, Punjab, India

    Parveen Singla

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  1. Vishal Gupta
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  2. Monish Gupta
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  3. Parveen Singla
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Correspondence to Vishal Gupta.

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Gupta, V., Gupta, M. & Singla, P. Ship Detection from Highly Cluttered Images Using Convolutional Neural Network. Wireless Pers Commun 121, 287–305 (2021). https://doi.org/10.1007/s11277-021-08635-5

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