Fishing and Military Ship Recognition using Parameters of Convolutional Neural Network

Syah, Adinda; Wulandari, Meirista; Gunawan, Dadang

doi:10.1109/icoiact50329.2020.9332062

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…[15,16], while various types of ships were classified in Refs. [17–19]. Above all, this work turned out to outperform others in respect of the accuracy.…”

Section: Resultsmentioning

confidence: 74%

“…The aim in Ref. [17] was to distinguish fishing from naval vessels, in Ref. [18] was to tell the difference among oil tankers, bulk carriers and container ships, while in Ref.…”

Section: Introductionmentioning

confidence: 99%

“…Similarly, deep-learning models were employed to classify moving targets on sea surface [17][18][19]. The aim in Ref.…”

Section: Introductionmentioning

confidence: 99%

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Development of Fishing Vessel Identification Model Based on Deep Neural Network

Lin

Chen

et al. 2022

IEEJ Transactions Elec Engng

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This paper presents a deep neural network (DNN)‐based model to recognize fishing vessels. In Taiwan, the vast majority of small fishing vessels are not equipped with an automatic identification system (AIS). As a consequence, the staff in a fishing port administration become heavily loaded when monitoring and managing the fishing vessels accessing a port. The workload is expected to be eased using this work. For the first time in the literature, a captured fishing vessel image was converted to a 128‐dimensional embedding for recognition purposes. The presented model gave a false positive rate (FPR) as low as 1.13% and an accuracy up to 99.47% at threshold = 0.772379. Finally, all the performance metrics, namely, the true positive rate (TPR), the FPR, precision and accuracy, are actually functions of the threshold which can be specified by users to meet specific requirements. © 2022 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC.

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“…[15,16], while various types of ships were classified in Refs. [17–19]. Above all, this work turned out to outperform others in respect of the accuracy.…”

Section: Resultsmentioning

confidence: 74%

“…The aim in Ref. [17] was to distinguish fishing from naval vessels, in Ref. [18] was to tell the difference among oil tankers, bulk carriers and container ships, while in Ref.…”

Section: Introductionmentioning

confidence: 99%

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Development of Fishing Vessel Identification Model Based on Deep Neural Network

Lin

Chen

et al. 2022

IEEJ Transactions Elec Engng

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“…The proposed solution presented in the study is to attack the problem on three fronts, improving the image acquisition hardware technology, creating an image preprocessing step, and increasing the dataset used for training, with images that have multiple targets and high diversity [137]. Regarding the issue of diversity, the use of datasets in the CNN models must have good image quality and also represent the shapes of the objects, which are taken from multiple sides [78].…”

Section: Datasetsmentioning

confidence: 99%

Literature Review on Ship Localization, Classification, and Detection Methods Based on Optical Sensors and Neural Networks

Teixeira

Araujo

Costa

et al. 2022

Sensors

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Object detection is a common application within the computer vision area. Its tasks include the classic challenges of object localization and classification. As a consequence, object detection is a challenging task. Furthermore, this technique is crucial for maritime applications since situational awareness can bring various benefits to surveillance systems. The literature presents various models to improve automatic target recognition and tracking capabilities that can be applied to and leverage maritime surveillance systems. Therefore, this paper reviews the available models focused on localization, classification, and detection. Moreover, it analyzes several works that apply the discussed models to the maritime surveillance scenario. Finally, it highlights the main opportunities and challenges, encouraging new research in this area.

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A Deep Learning-based ROV Surveillance System for Monitoring Illegal Fishing

Huang,

Chen

2023

2023 IEEE 12th Global Conference on Consumer Electronics (GCCE)

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Fishing and Military Ship Recognition using Parameters of Convolutional Neural Network

Cited by 3 publications

References 6 publications

Development of Fishing Vessel Identification Model Based on Deep Neural Network

Development of Fishing Vessel Identification Model Based on Deep Neural Network

Literature Review on Ship Localization, Classification, and Detection Methods Based on Optical Sensors and Neural Networks

A Deep Learning-based ROV Surveillance System for Monitoring Illegal Fishing

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