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

Teixeira, Eduardo H.; Araujo, Beatriz; Costa, Victor; Mafra, Samuel; Figueiredo, Felipe Augusto Pereira de

doi:10.3390/s22186879

Cited by 15 publications

(9 citation statements)

References 153 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Today, it is seen that the use of optical systems in maritime surveillance applications based on artificial intelligence has become widespread. More successful results are obtained daily in using data from optical systems for object detection and identification with image databases and machine learning modelling [66].…”

Section: Evaluation Of Alternativesmentioning

confidence: 99%

“…The features of maritime big data are of paramount importance in the continuous tracking and detection of threats to ensure effective maritime surveillance. This criterion is considered as the cornerstone of the data management process in the literature, particularly emphasizing the necessity of data possessing various attributes for achieving the intended purpose in artificial intelligence-based maritime surveillance applications and anomaly detection [14,[62][63][64][65][66]. In the Copernicus maritime surveillance system and projects such as ScanMaris, Marine-EO, I2C, Compass2020, SafeShore, Effector, Ranger, and Promenade, data characteristics are frequently considered.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Safety–Security Analysis of Maritime Surveillance Systems in Critical Marine Areas

Şengül,

Yılmaz,

Uğurlu

2023

Sustainability

View full text Add to dashboard Cite

In today’s world, wherein more than 80% of world trade is carried out by maritime routes, the safety and security of the seas where this trade takes place is of vast importance for nations and the international community. For this reason, ensuring the sustainable safety and security of the seas has become an integral part of the mission of all maritime-related entities. Using big data extracted from the seas and maritime activities into meaningful information with artificial intelligence applications and developing applications that can be used in maritime surveillance will be of great importance for augmenting maritime safety and security. In this article, data sources which can be used by a maritime surveillance system based on big data and artificial intelligence technologies and which can be established around sensitive sea areas and critical coastal facilities, are identified and a model proposal using this maritime big data is put forward.

show abstract

Section: Evaluation Of Alternativesmentioning

confidence: 99%

mentioning

confidence: 99%

Safety–Security Analysis of Maritime Surveillance Systems in Critical Marine Areas

Şengül,

Yılmaz,

Uğurlu

2023

Sustainability

View full text Add to dashboard Cite

show abstract

“…Research methods for ship target detection can be broadly categorized into traditional methods and those based on computer vision [5]. Traditional ship recognition heavily relies on manual monitoring, with watchtower surveillance being a critical component [6].…”

Section: Introductionmentioning

confidence: 99%

SSMA-YOLO: A Lightweight YOLO Model with Enhanced Feature Extraction and Fusion Capabilities for Drone-Aerial Ship Image Detection

Han,

Guo,

Yang

et al. 2024

Drones

View full text Add to dashboard Cite

Due to the unique distance and angles involved in satellite remote sensing, ships appear with a small pixel area in images, leading to insufficient feature representation. This results in suboptimal performance in ship detection, including potential misses and false detections. Moreover, the complexity of backgrounds in remote sensing images of ships and the clustering of vessels also adversely affect the accuracy of ship detection. Therefore, this paper proposes an optimized model named SSMA-YOLO, based on YOLOv8n. First, this paper introduces a newly designed SSC2f structure that incorporates spatial and channel convolution (SCConv) and spatial group-wise enhancement (SGE) attention mechanisms. This design reduces spatial and channel redundancies within the neural network, enhancing detection accuracy while simultaneously reducing the model’s parameter count. Second, the newly designed MC2f structure employs the multidimensional collaborative attention (MCA) mechanism to efficiently model spatial and channel features, enhancing recognition efficiency in complex backgrounds. Additionally, the asymptotic feature pyramid network (AFPN) structure was designed for progressively fusing multi-level features from the backbone layers, overcoming challenges posed by multi-scale variations. Experiments of the ships dataset show that the proposed model achieved a 4.4% increase in mAP compared to the state-of-the-art single-stage target detection YOLOv8n model while also reducing the number of parameters by 23%.

show abstract

“…Leveraging cameras in these locations presents an opportunity to develop intelligent applications employing Computer Vision (CV) techniques. Cameras offer a cost-effective alternative for low-energy applications, particularly when coupled with efficient, fast, and robust detection techniques using embedded systems with CV capabilities [2,3].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Edge Computing Object Detection Models for Maritime Surveillance on Raspberry Pi

V. L. B. Silva,

Pereira de Figueiredo,

B. Mafra

et al. 2024

Preprint

View full text Add to dashboard Cite

The exponential growth of maritime traffic has brought with it a multitude of business opportunities, while at the same time posing unprecedented challenges in terms of control and surveillance. Traditional methods struggle to cope with the sheer volume and complexity of maritime activities, which calls for innovative solutions. This study presents a comparative analysis of three prominent computing object detection models applied to maritime surveillance. By evaluating their performance metrics in dynamic maritime environments, this study provides information on the most suitable model for effective maritime monitoring applications, attaining an impressive 91\% mean Average Precision (mAP) using the YOLOv5n model.

show abstract

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

Cited by 15 publications

References 153 publications

Safety–Security Analysis of Maritime Surveillance Systems in Critical Marine Areas

Safety–Security Analysis of Maritime Surveillance Systems in Critical Marine Areas

SSMA-YOLO: A Lightweight YOLO Model with Enhanced Feature Extraction and Fusion Capabilities for Drone-Aerial Ship Image Detection

Performance Evaluation of Edge Computing Object Detection Models for Maritime Surveillance on Raspberry Pi

Contact Info

Product

Resources

About