A novel ship classification network with cascade deep features for line-of-sight sea data

Uçar, Ferhat; Korkmaz, Deniz

doi:10.1007/s00138-021-01198-2

Cited by 13 publications

(4 citation statements)

References 39 publications

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“…The military and cruise ship classes were not included. Ucar and Korkmaz [31] obtained high accuracy in the military and cruise ship classes compared with our proposed method with a difference of 0.38%, but for the other classes of ships, it acquired low classification accuracy compared with our proposed method. For overall comparison, our proposed approach performed better than others.…”

Section: Comparison Of Proposed and Existing Methodsmentioning

confidence: 76%

Ship Classification Based on Improved Convolutional Neural Network Architecture for Intelligent Transport Systems

Leonidas

Yang

2021

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In recent years, deep learning has been used in various applications including the classification of ship targets in inland waterways for enhancing intelligent transport systems. Various researchers introduced different classification algorithms, but they still face the problems of low accuracy and misclassification of other target objects. Hence, there is still a need to do more research on solving the above problems to prevent collisions in inland waterways. In this paper, we introduce a new convolutional neural network classification algorithm capable of classifying five classes of ships, including cargo, military, carrier, cruise and tanker ships, in inland waterways. The game of deep learning ship dataset, which is a public dataset originating from Kaggle, has been used for all experiments. Initially, the five pretrained models (which are AlexNet, VGG, Inception V3 ResNet and GoogleNet) were used on the dataset in order to select the best model based on its performance. Resnet-152 achieved the best model with an accuracy of 90.56%, and AlexNet achieved a lower accuracy of 63.42%. Furthermore, Resnet-152 was improved by adding a classification block which contained two fully connected layers, followed by ReLu for learning new characteristics of our training dataset and a dropout layer to resolve the problem of a diminishing gradient. For generalization, our proposed method was also tested on the MARVEL dataset, which consists of more than 10,000 images and 26 categories of ships. Furthermore, the proposed algorithm was compared with existing algorithms and obtained high performance compared with the others, with an accuracy of 95.8%, precision of 95.83%, recall of 95.80%, specificity of 95.07% and F1 score of 95.81%.

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Section: Comparison Of Proposed and Existing Methodsmentioning

confidence: 76%

Ship Classification Based on Improved Convolutional Neural Network Architecture for Intelligent Transport Systems

Leonidas

Yang

2021

Information

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“…For instance, Ucar and Korkmaz. [78] employed a transfer learning method in the feature extraction phase to construct a novel CNN network structure, employing a cascaded approach to incorporate additional feature information for object detection. Similarly, Salem et al [79] initially performed pre-training on ImageNet using eight common network structures, followed by finetuning the model using the acquired weights.…”

Section: Deep Learning For Object Detectionmentioning

confidence: 99%

Section: Deep Learning For Object Detectionmentioning

confidence: 99%

A review of intelligent ship marine object detection based on RGB camera

Yang,

Solihin,

Zhao

et al. 2023

IET Image Processing

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The article presents a comprehensive summary of Intelligent Ship Marine Object Detection (ISMOD) based on the RGB Camera. Marine object detection plays a pivotal role in enabling intelligent ships to acquire crucial data and security assurances for autonomous navigation. Among the various detection sensors, the RGB Camera is an informative and cost‐effective tool with a wide range of civil applications. In the beginning, the ISMOD metrics based on the RGB camera is analyzed from three significant aspects, namely accuracy, speed, and robustness. Subsequently, the latest research status and comparative overview are presented, encompassing three mainstream detection methods: traditional detection, deep learning detection, and sensor fusion detection. Finally, the existing challenges of ISMOD are discussed and future development trends are recommended. The results demonstrate that forthcoming development will predominantly concentrate on deep learning approaches, complemented by other techniques. It is imperative to advance detection performance in domains such as deep fusion, multi‐feature extraction, multi‐fusion technology, and lightweight detection architecture.

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“…It is a supervised and filter-based feature selection algorithm. The selection of features stands on the general characteristics of the training data without any classifier [17]. As a supervised algorithm, MUTInf assesses both the correlation of a features subset to the defined class and the redundancy to other data.…”

Section: Mutual Information Feature Selection (Mutinf)mentioning

confidence: 99%

Implementation of Multiple Feature Selection Algorithms for Speech Spoofing Detection

Zaidi

2022

J. Phys.: Conf. Ser.

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The ASVspoof challenge sequences were proposed to lead the research in anti-spoofing to a new level for automatic speaker verification (ASV). It’s verified that constant Q cepstral coefficients (CQCC) processes speech in variable frequencies with adjustable resolution and outperforms the other generally used features and Linear Frequency Cepstral Coefficient (LFCC) is used in high-frequency areas. The feature selection algorithm is offered to decrease computational complexity and overfitting for spoofed utterance detection. Precisely, there’s a demand for feature selection algorithms that are computationally effective and sensitive to feature interactions so that useful features aren’t falsely excluded during the ranking process. We experiment on the ASVspoof 2019 challenge for the assessment of spoofing countermeasures. After the evaluation of our given algorithms and data gives us an equal error rate (EER) and tandem discovery cost function (t-DCF) values. Experimental results on ASVspoof 2019 physical access referring to multiple feature selection approaches show a breakthrough compared to the baseline.

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A novel ship classification network with cascade deep features for line-of-sight sea data

Cited by 13 publications

References 39 publications

Ship Classification Based on Improved Convolutional Neural Network Architecture for Intelligent Transport Systems

Ship Classification Based on Improved Convolutional Neural Network Architecture for Intelligent Transport Systems

A review of intelligent ship marine object detection based on RGB camera

Implementation of Multiple Feature Selection Algorithms for Speech Spoofing Detection

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