Maritime Environment Perception Based on Deep Learning

Lin, Jiaying; Diekmann, Phillip; Framing, Christian-Eike; Zweigel, René; Abel, Dirk

doi:10.1109/tits.2022.3140933

Cited by 23 publications

(6 citation statements)

References 27 publications

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“…12 Accuracy Kalman filters [113,115] 13. Decision support Several [58,68,76,89,92,93,105,108,128,130,161,254,255] 14.…”

Section: Neural Networkmentioning

confidence: 99%

Enhancing Maritime Cybersecurity through Operational Technology Sensor Data Fusion: A Comprehensive Survey and Analysis

Potamos,

Stavrou,

Stavrou

2024

Sensors

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Cybersecurity is becoming an increasingly important aspect in ensuring maritime data protection and operational continuity. Ships, ports, surveillance and navigation systems, industrial technology, cargo, and logistics systems all contribute to a complex maritime environment with a significant cyberattack surface. To that aim, a wide range of cyberattacks in the maritime domain are possible, with the potential to infect vulnerable information and communication systems, compromising safety and security. The use of navigation and surveillance systems, which are considered as part of the maritime OT sensors, can improve maritime cyber situational awareness. This survey critically investigates whether the fusion of OT data, which are used to provide maritime situational awareness, may also improve the ability to detect cyberincidents in real time or near-real time. It includes a thorough analysis of the relevant literature, emphasizing RF but also other sensors, and data fusion approaches that can help improve maritime cybersecurity.

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“…12 Accuracy Kalman filters [113,115] 13. Decision support Several [58,68,76,89,92,93,105,108,128,130,161,254,255] 14.…”

Section: Neural Networkmentioning

confidence: 99%

Enhancing Maritime Cybersecurity through Operational Technology Sensor Data Fusion: A Comprehensive Survey and Analysis

Potamos,

Stavrou,

Stavrou

2024

Sensors

View full text Add to dashboard Cite

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“…Conventional image‐processing methods (Jin et al, 2020; Park et al, 2015; Sinisterra et al, 2017; Zhang et al, 2017) or deep learning‐based detection methods (Chen et al, 2021; Hammedi et al, 2019; Lee et al, 2021; Liu et al, 2021; Moosbauer et al, 2019; Nita & Vandewal, 2020; Spraul et al, 2020) have been suggested, and some studies (Bovcon et al, 2021; Prasad et al, 2017; Shao et al, 2018) have provided data sets for training and validating the object‐detection algorithms. LiDAR (Jeong & Li, 2021; Lin et al, 2022; Thompson et al, 2019) and radar (Almeida et al, 2009; Ha et al, 2021; Im et al, 2021) have also been used to detect maritime obstacles. Unlike the camera which is technically a bearing‐only sensor, LiDAR and radar provide both range and bearing measurements.…”

Section: Related Workmentioning

confidence: 99%

Field experiment of autonomous ship navigation in canal and surrounding nearshore environments

Kim,

Lee,

Chung

et al. 2023

Journal of Field Robotics

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In this paper, we present the development of autonomous navigation capabilities for small cruise boats, and their verification by field experiments in a canal and its surrounding waters. A cruise boat was converted to an autonomous surface vehicle (ASV) by installing various sensors and actuators to enable autonomous navigation. Navigation and perception sensors, such as global positioning system, attitude and heading reference system, radar, light detection and ranging (LiDAR), and cameras, were mounted on the ASV to estimate its motion and perceive the surrounding environment. Motors and potentiometers were installed for active control of the ASV. Software system components including navigation filters, object‐detection, path‐planning, and control algorithms were designed and implemented. In the narrow canal region, LiDARs were used to detect the side walls and boundaries of the canal. In open areas outside the canal, obstacles and object features were detected using various combinations of onboard sensors. A model‐based path‐planning algorithm was designed to avoid the detected obstacles, and the line‐of‐sight guidance was employed to control the vehicle. The performance of the developed system was verified through a field experiment in a real‐world maritime environment.

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“…These algorithms must be robust and reliable, and thus multisensor systems have been proposed to perceive maritime obstacles. Accordingly, a perception set of radar, lidar, wide‐field stereo camera, and 360 electro‐optical camera is widely implemented in various types of USVs (Elkins et al, 2010; Han et al, 2020; Helgesen et al, 2022; Lin et al, 2022; Liu et al, 2022). However, most existing studies have been conducted by using only multiple sensors mounted on USV platforms.…”

Section: Related Workmentioning

confidence: 99%

Field demonstration of advanced autonomous navigation technique for a fully unmanned surface vehicle in complex coastal traffic areas

Han¹,

Park²,

Park³

et al. 2023

Journal of Field Robotics

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This study introduces an advanced autonomous navigation algorithm for unmanned surface vehicle (USV) operations in complex coastal traffic areas. To facilitate the undertaking of tests for innovative technologies, the Korean government has designated regulation‐free special zones, thereby enabling field tests for USV demonstrations without an onboard safety person on real manned‐ship navigation routes. To enable real‐world USV operation without onboard safety person, an existing autonomous navigation algorithm is extended to achieve sufficiently reliable and robust USV operation. Accordingly, berthing and unberthing algorithms are newly developed for unmanned operation, and enhanced situational awareness algorithms are applied to detect all types of obstacles, including small‐sized floating objects spread over a wide range, such as a fish farm. In particular, the Korean intelligent maritime transportation service, called e‐Navigation, is incorporated into the autonomous navigation algorithms to overcome the limitations of vehicle autonomy based on onboard sensors. To demonstrate the capability of fully unmanned operation in real marine traffic scenarios, long‐distance navigation (54 km on a single voyage) was conducted by running several missions, including navigation in complex fish farm areas, maritime surveys, collision avoidance, navigation on real traffic routes, and emergency response. The extended navigation algorithms and results of the field tests are presented and discussed in this paper.

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Maritime Environment Perception Based on Deep Learning

Cited by 23 publications

References 27 publications

Enhancing Maritime Cybersecurity through Operational Technology Sensor Data Fusion: A Comprehensive Survey and Analysis

Enhancing Maritime Cybersecurity through Operational Technology Sensor Data Fusion: A Comprehensive Survey and Analysis

Field experiment of autonomous ship navigation in canal and surrounding nearshore environments

Field demonstration of advanced autonomous navigation technique for a fully unmanned surface vehicle in complex coastal traffic areas

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