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Satellite imagery-based Airbus ship localization and detection using deep learning-based approaches

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Abstract

Due to the increasing ship trafficking, illegal fishing, cargo crimes, drug crimes, identifying people in the sea, shipwrecked etc., it is essential to detect ships to monitor and control maritime crime. Researchers have been working constantly and facing challenges to automatically detect ships, mainly in remote sensing, which has shown a great impact in providing safety, detecting illegal trade, pollution, and oil slick monitoring. In this paper, 192,555 satellite images of ships have been used to train the deep learning models such as customized CNN, ResNet152V2, InceptionResNetV2, MobileNetV2, NasNetLarge, DenseNet201, and EfficientNetB7. The dataset has been first pre-processed to remove any noisy signals before being graphically represented using exploratory data analysis to study the pixel intensities of data. Later, feature extraction techniques such as image masking, image augmentation, and contour features are applied to create bounding boxes corresponding to the detected ship’s localizing coordinates. The models are further evaluated using the ship detected and ship not detected classes. It has been discovered that customized CNN achieved the highest precision by 96.16 as well as 95.64 and F1 score value by 99.89 and 95.99 in terms of both the classes, respectively. In contrast, DenseNet201 had the highest recall 95.46, and 93.45, for the ship, detected and not detected classes, respectively).

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Authors and Affiliations

  1. Guru Gobind Singh Indraprastha University, Dwarka, New Delhi, India

    Jigyasa Chadha

  2. Netaji Subhash University of Technology, New Delhi, India

    Aarti Jain

  3. Department of CSE, School of Technology, Pandit Deendayal Energy University, Gandhinagar, Gujarat, India

    Yogesh Kumar

Authors
  1. Jigyasa Chadha
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  2. Aarti Jain
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  3. Yogesh Kumar
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Contributions

The first author has drafted the article, whereas the second author has drawn the figures and result tables. The third author has performed the training and validation for Ship Localization and Detection using deep learning.

Corresponding author

Correspondence to Yogesh Kumar.

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Chadha, J., Jain, A. & Kumar, Y. Satellite imagery-based Airbus ship localization and detection using deep learning-based approaches. Peer-to-Peer Netw. Appl. 16, 1481–1498 (2023). https://doi.org/10.1007/s12083-023-01493-x

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