Deep Convolutional Neural Network based Ship Images Classification

Mishra, Narendra Kumar; Kumar, Ashok J.; Choudhury, Kishor

doi:10.14429/dsj.71.16236

Cited by 13 publications

(6 citation statements)

References 6 publications

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“…The authors effectively showcased the efficacy of their method for ship image classification. [32] utilized a VGG model for a dataset of 2,400 images, classifying ships into four categories, encompassing both military and civilian vessels. They highlighted how data augmentation and fine-tuning of the VGG architecture contributed to improved model performance.…”

Section: Literature Reviewmentioning

confidence: 99%

Explainable Deep Learning to Classify Royal Navy Ships

Baesens,

Adams,

Pacheco-Ruiz

et al. 2024

IEEE Access

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We research how deep learning convolutional neural networks can be used to to automatically classify the unique data set of black-and-white naval ships images from the Wright and Logan photographic collection held by the National Museum of the Royal Navy. We contrast various types of deep learning methods: pretrained models such as ConvNeXt, ResNet and EfficientNet, and ConvMixer. We also thoroughly investigate the impact of data preprocessing and externally obtained images on model performance. Finally, we research how the models estimated can be made transparent using visually appealing interpretability techniques such as Grad-CAM. We find that ConvNeXt has the best performance for our data set achieving an accuracy of 79.62% for 0-notch classification and an impressive 94.86% for 1-notch classification. The results indicate the importance of appropriate image preprocessing. Image segmentation combined with soft augmentation significantly contributes to model performance. We consider this research to be original in several aspects. Notably, it distinguishes itself through the uniqueness of the acquired dataset. Additionally, its distinctiveness extends to the analytical modeling pipeline, which encompasses a comprehensive range of modeling steps, including data preprocessing (incorporating external data, image segmentation, and image augmentation) and the use of deep learning techniques such as ConvNeXt, ResNet, EfficientNet, and ConvMixer. Furthermore, the research employs explanatory tools like Grad-CAM to enhance model interpretability and usability. We believe the proposed methodology offers lots of potential for documenting historic image collections.

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Section: Literature Reviewmentioning

confidence: 99%

Explainable Deep Learning to Classify Royal Navy Ships

Baesens,

Adams,

Pacheco-Ruiz

et al. 2024

IEEE Access

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“…In [57], a Bayesian transformer neural network (BTNN) was used to identify ship type based on ship motion Furthermore, a VGG16 CNN has been used in [59] for ship classification from images. Gradient descent has been used for error minimization while the data was divided into training and test data sets in 80% to 20% ratio, respectively.…”

Section: A Ship Image Classification and Type Identificationsmentioning

confidence: 99%

A Review of Artificial Neural Networks Applications in Maritime Industry

Assani,

Matić,

Kaštelan

et al. 2023

IEEE Access

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Artificial neural networks (ANN) are a data driven tool that has been used for modeling, prediction, optimization, classification, diagnostics, decision-making, etc., in various systems where measurements are available to produce significant amount of data. Ship processes are constantly monitored in order to control the operation of the ship and to ensure efficient and safe environment, generating large amount of data. Those data are increasingly being exploited by ANNs and the number of applications is growing. The aim of this paper is to analyze the applications of ANNs in maritime industry, and especially on ships. Based on the review analysis of the sixty-nine papers found published on this topic over the last 10 years in relevant databases, applications have been classified into eight categories in this paper. ANN types, training algorithms, activation functions, as well as measures used to evaluate the performance of the ANN models, have been analyzed for each application category. ANNs rely on data, therefore data acquisition, data processing, organization of the data for training ANN models, their validation and testing have also been addressed in this paper. The conclusions from the review analysis presented should be useful for future work in the area of ANN applications on ships and in maritime industry.INDEX TERMS Applications of artificial neural networks (ANN), ANN types, activation functions, evaluation measures, maritime industry, review analysis, ships, training algorithms

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“…Many studies consider the classification of vessels by training convolutional neural networks with images of different types of vessels. Recent studies are Mishra et al [16] and Liu et al [17]. However, this type of classification does not use the information for route planning, as real-time image classification requires a high-resolution image that can only be acquired within a few miles of the vessel.…”

Section: B Operational Issues and Our Approachmentioning

confidence: 99%

Time-Critical Maritime UAV Mission Planning Using a Neural Network: An Operational View

et al. 2022

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An operational planning procedure for a time-critical maritime unmanned aerial vehicle (UAV) search mission is introduced and evaluated. The mission is the fast identification of a target vessel. The triggering report only contains information regarding the category and displacement of a vessel carrying out a prohibited activity, resembling operational situations. A neural network trained to classify vessels is combined with vessel clustering to reduce waypoints in the flight plan. The UAV's onboard sensors provide input for the neural network regarding each vessel in the search area, resulting in a prioritization of vessels to be visited. As the accuracy of the classification and the possibilities for clustering depend on several operational factors as well as on the UAV's sensor degradation, we investigate three methodologies to identify which planning procedure to use in various operational situations. The results show that our robust and agile approach can help a UAV find the unknown target vessel as soon as possible.INDEX TERMS Artificial intelligence, optimization methods, unmanned aerial vehicles (UAV), decision support systems.

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Deep Convolutional Neural Network based Ship Images Classification

Cited by 13 publications

References 6 publications

Explainable Deep Learning to Classify Royal Navy Ships

Explainable Deep Learning to Classify Royal Navy Ships

A Review of Artificial Neural Networks Applications in Maritime Industry

Time-Critical Maritime UAV Mission Planning Using a Neural Network: An Operational View

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