A Review of Methods for Ship Detection with Electro-Optical Images in Marine Environments

Wang, Liqian; Fan, Shuzhen; Li, Yongfu; Cheng, Fei; Liu, Junliang; Liu, Bohan; Dong, Yaru; Liu, Zhaojun; Xian, Zhao

doi:10.3390/jmse9121408

Cited by 16 publications

(7 citation statements)

References 71 publications

(134 reference statements)

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“…Being a non-cooperative source, it does not rely on the vessels themselves for receiving data. Additionally, since such data are collected by satellites, global coverage can be considered a possibility [43,44]. Of course, as in all cases, relying solely on satellite imagery for monitoring an area comes with its own disadvantages.…”

Section: Satellite Imagesmentioning

confidence: 99%

See 1 more Smart Citation

The Big Picture: An Improved Method for Mapping Shipping Activities

Troupiotis-Kapeliaris,

Zissis,

Bereta

et al. 2023

Remote Sensing

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Density maps support a bird’s eye view of vessel traffic, through providing an overview of vessel behavior, either at a regional or global scale in a given timeframe. However, any inaccuracies in the underlying data, due to sensor noise or other factors, evidently lead to erroneous interpretations and misleading visualizations. In this work, we propose a novel algorithmic framework for generating highly accurate density maps of shipping activities, from incomplete data collected by the Automatic Identification System (AIS). The complete framework involves a number of computational steps for (1) cleaning and filtering AIS data, (2) improving the quality of the input dataset (through trajectory reconstruction and satellite image analysis) and (3) computing and visualizing the subsequent vessel traffic as density maps. The framework describes an end-to-end implementation pipeline for a real world system, capable of addressing several of the underlying issues of AIS datasets. Real-world data are used to demonstrate the effectiveness of our framework. These experiments show that our trajectory reconstruction method results in significant improvements up to 15% and 26% for temporal gaps of 3–6 and 6–24 h, respectively, in comparison to the baseline methodology. Additionally, a use case in European waters highlights our capability of detecting “dark vessels”, i.e., vessel positions not present in the AIS data.

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Section: Satellite Imagesmentioning

confidence: 99%

“…In the related literature, the problem of vessel detection in satellite imagery has been the focus of many academic and industrial research activities [43,44]. These activities can be classified into two broad categories:…”

Section: Satellite Imagesmentioning

confidence: 99%

The Big Picture: An Improved Method for Mapping Shipping Activities

Troupiotis-Kapeliaris,

Zissis,

Bereta

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Third, many works used the horizon to segment the image (into sea and non-sea region). In the same context, the horizon feature provides computational benefits to the object detection task by reducing the search region down to that around the horizon or by eliminating the sky pixels that will obviously not depict any obstacle [5,[18][19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. Fourth, even after the detection of maritime objects on the image coordinates, some researchers used the horizon line to geo-localize surrounding objects and obstacles [36][37][38][39], i.e., find the distance in meters from the camera to the obstacle.…”

Section: Applications Of the Horizon Linementioning

confidence: 99%

A Horizon Line Annotation Software for Streamlining Autonomous Sea Navigation Experiments

Zardoua,

Beguiel Bergor,

El Wahabi

et al. 2023

AAIML

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Horizon line (or sea line) detection (HLD) is a critical component in multiple marine autonomous navigation tasks, such as identifying the navigation area (i.e., the sea), obstacle detection and geo-localization, and digital video stabilization. A recent survey highlighted several weaknesses of such detectors, particularly on sea conditions lacking from the most extensive dataset currently used by HLD researchers. Experimental validation of more robust HLDs involves collecting an extensive set of these lacking sea conditions and annotating each collected image with the correct position and orientation of the horizon line. The annotation 1768

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“…The point cloud dataset produced by LiDAR is, however, limited in scope and lacking in detail, and the direct processing of the 3D point cloud necessitates powerful computational hardware [4]. While the optical image of the water surface has the benefits of being simple to collect, having rich color and texture information, and having established processing techniques [5], using optoelectronic equipment to obtain the intended appearance attributes is a crucial part of how USVs perceive their surroundings.…”

Section: Introductionmentioning

confidence: 99%

A Lightweight Detection Algorithm for Unmanned Surface Vehicles Based on Multi-Scale Feature Fusion

Zhang

et al. 2023

JMSE

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Lightweight detection methods are frequently utilized for unmanned system sensing; however, when put in complicated water surface environments, they suffer from insufficient feature fusion and decreased accuracy. This paper proposes a lightweight surface target detection algorithm with multi-scale feature fusion augmentation in an effort to improve the poor detection accuracy of lightweight detection algorithms in the mission environment of unmanned surface vehicles (USVs). Based on the popular one-stage lightweight YOLOv7-Tiny target detection algorithms, a lightweight extraction module is designed first by introducing the multi-scale residual module to reduce the number of parameters and computational complexity while improving accuracy. The Mish and SiLU activation functions are used to enhance network feature extraction. Second, the path aggregation network employs coordinate convolution to strengthen spatial information perception. Finally, the dynamic head, which is based on the attention mechanism, improves the representation ability of object detection heads without any computational overhead. According to the experimental findings, the proposed model has 22.1% fewer parameters than the original model, 15% fewer GFLOPs, a 6.2% improvement in mAP@0.5, a 4.3% rise in mAP@0.5:0.95, and satisfies the real-time criteria. According to the research, the suggested lightweight water surface detection approach includes a lighter model, a simpler computational architecture, more accuracy, and a wide range of generalizability. It performs better in a variety of difficult water surface circumstances.

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A Review of Methods for Ship Detection with Electro-Optical Images in Marine Environments

Cited by 16 publications

References 71 publications

The Big Picture: An Improved Method for Mapping Shipping Activities

The Big Picture: An Improved Method for Mapping Shipping Activities

A Horizon Line Annotation Software for Streamlining Autonomous Sea Navigation Experiments

A Lightweight Detection Algorithm for Unmanned Surface Vehicles Based on Multi-Scale Feature Fusion

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