54 research outputs found
Sea-Surface Object Detection Based on Electro-Optical Sensors: A Review
Sea-surface object detection is critical for navigation safety of autonomous ships. Electrooptical (EO) sensors, such as video cameras, complement radar on board in detecting small obstacle
sea-surface objects. Traditionally, researchers have used horizon detection, background subtraction, and
foreground segmentation techniques to detect sea-surface objects. Recently, deep learning-based object
detection technologies have been gradually applied to sea-surface object detection. This article demonstrates a comprehensive overview of sea-surface object-detection approaches where the advantages
and drawbacks of each technique are compared, covering four essential aspects: EO sensors and image
types, traditional object-detection methods, deep learning methods, and maritime datasets collection. In
particular, sea-surface object detections based on deep learning methods are thoroughly analyzed and
compared with highly influential public datasets introduced as benchmarks to verify the effectiveness of
these approaches. The arti
Aeolus Ocean -- A simulation environment for the autonomous COLREG-compliant navigation of Unmanned Surface Vehicles using Deep Reinforcement Learning and Maritime Object Detection
Heading towards navigational autonomy in unmanned surface vehicles (USVs) in
the maritime sector can fundamentally lead towards safer waters as well as
reduced operating costs, while also providing a range of exciting new
capabilities for oceanic research, exploration and monitoring. However,
achieving such a goal is challenging. USV control systems must, safely and
reliably, be able to adhere to the international regulations for preventing
collisions at sea (COLREGs) in encounters with other vessels as they navigate
to a given waypoint while being affected by realistic weather conditions,
either during the day or at night. To deal with the multitude of possible
scenarios, it is critical to have a virtual environment that is able to
replicate the realistic operating conditions USVs will encounter, before they
can be implemented in the real world. Such "digital twins" form the foundations
upon which Deep Reinforcement Learning (DRL) and Computer Vision (CV)
algorithms can be used to develop and guide USV control systems. In this paper
we describe the novel development of a COLREG-compliant DRL-based collision
avoidant navigational system with CV-based awareness in a realistic ocean
simulation environment. The performance of the trained autonomous Agents
resulting from this approach is evaluated in several successful navigations to
set waypoints in both open sea and coastal encounters with other vessels. A
binary executable version of the simulator with trained agents is available at
https://github.com/aavek/Aeolus-OceanComment: 22 pages, last blank page, 17 figures, 1 table, color, high
resolution figure
Advancing fishery-independent stock assessments for the Norway lobster (Nephrops norvegicus) with new monitoring technologies
The Norway lobster, Nephrops norvegicus, supports a key European fishery. Stock assessments for this species are mostly based on trawling and UnderWater TeleVision (UWTV) surveys. However, N. norvegicus are burrowing organisms and these survey methods are unable to sample or observe individuals in their burrows. To account for this, UWTV surveys generally assume that "1 burrow system = 1 animal", due to the territorial behavior of N. norvegicus. Nevertheless, this assumption still requires in-situ validation. Here, we outline how to improve the accuracy of current stock assessments for N. norvegicus with novel ecological monitoring technologies, including: robotic fixed and mobile camera-platforms, telemetry, environmental DNA (eDNA), and Artificial Intelligence (AI). First, we outline the present status and threat for overexploitation in N. norvegicus stocks. Then, we discuss how the burrowing behavior of N. norvegicus biases current stock assessment methods. We propose that state-of-the-art stationary and mobile robotic platforms endowed with innovative sensors and complemented with AI tools could be used to count both animals and burrows systems in-situ, as well as to provide key insights into burrowing behavior. Next, we illustrate how multiparametric monitoring can be incorporated into assessments of physiology and burrowing behavior. Finally, we develop a flowchart for the appropriate treatment of multiparametric biological and environmental data required to improve current stock assessment methods
Advancing fishery-independent stock assessments for the Norway lobster (Nephrops norvegicus) with new monitoring techn
The Norway lobster, Nephrops norvegicus, supports a key European fishery.
Stock assessments for this species are mostly based on trawling and
UnderWater TeleVision (UWTV) surveys. However, N. norvegicus are
burrowing organisms and these survey methods are unable to sample or
observe individuals in their burrows. To account for this, UWTV surveys
generally assume that “1 burrow system = 1 animal”, due to the territorial
behavior of N. norvegicus. Nevertheless, this assumption still requires in-situ
validation. Here, we outline how to improve the accuracy of current stock
assessments for N. norvegicus with novel ecological monitoring technologies,
including: robotic fixed and mobile camera-platforms, telemetry,
environmental DNA (eDNA), and Artificial Intelligence (AI). First, we outline
the present status and threat for overexploitation in N. norvegicus stocks. Then,
we discuss how the burrowing behavior of N. norvegicus biases current stock
assessment methods. We propose that state-of-the-art stationary and mobile
robotic platforms endowed with innovative sensors and complemented with AI
tools could be used to count both animals and burrows systems in-situ, as well
as to provide key insights into burrowing behavior. Next, we illustrate how
multiparametric monitoring can be incorporated into assessments of
physiology and burrowing behavior. Finally, we develop a flowchart for the
appropriate treatment of multiparametric biological and environmental data
required to improve current stock assessment methods
Advancing fishery-independent stock assessments for the Norway lobster (Nephrops norvegicus) with new monitoring technologies
The Norway lobster, Nephrops norvegicus, supports a key European fishery. Stock assessments for this species are mostly based on trawling and UnderWater TeleVision (UWTV) surveys. However, N. norvegicus are burrowing organisms and these survey methods are unable to sample or observe individuals in their burrows. To account for this, UWTV surveys generally assume that “1 burrow system = 1 animal”, due to the territorial behavior of N. norvegicus. Nevertheless, this assumption still requires in-situ validation. Here, we outline how to improve the accuracy of current stock assessments for N. norvegicus with novel ecological monitoring technologies, including: robotic fixed and mobile camera-platforms, telemetry, environmental DNA (eDNA), and Artificial Intelligence (AI). First, we outline the present status and threat for overexploitation in N. norvegicus stocks. Then, we discuss how the burrowing behavior of N. norvegicus biases current stock assessment methods. We propose that state-of-the-art stationary and mobile robotic platforms endowed with innovative sensors and complemented with AI tools could be used to count both animals and burrows systems in-situ, as well as to provide key insights into burrowing behavior. Next, we illustrate how multiparametric monitoring can be incorporated into assessments of physiology and burrowing behavior. Finally, we develop a flowchart for the appropriate treatment of multiparametric biological and environmental data required to improve current stock assessment methods
Digital semaphore: technical feasibility of QR code optical signaling for fleet communications
In recent decades, optical LOS communications such as flag semaphore or flashing light have atrophied to the point where, if they are required, U.S. Naval forces are at a distinct disadvantage. RF communications have become critical to nearly all operations, but this capability comes at the cost of disclosing the location of operations. Depending on the platform, these RF communications can become a critical vulnerability. EMCON attempts to minimize this vulnerability through the elimination of any RF emissions from a ship, but communication requirements in recent years have essentially prevented a complete suppression of RF emissions. This work proposes mitigating emissions vulnerability by utilizing a new method of optical communications at LOS visual ranges reminiscent of flag semaphore. Tactical QR code communications streaming digital data through optical signaling has the potential to provide tactical communications at a moderate range, allowing critical communications to be relayed to and from off-ship platforms. Additional technological advances can be used to overcome current range, security, reliability, and throughput barriers. This project demonstrates how a combination of essential technical capabilities can be used to establish a QR code communications system as a potentially useful approach for tactical operations.http://archive.org/details/digitalsemaphore1094534699Outstanding ThesisLieutenant Commander, United States NavyApproved for public release; distribution is unlimited
Unmanned Aerial Vehicles (UAVs): Collision Avoidance Systems and Approaches
Moving towards autonomy, unmanned vehicles rely heavily on state-of-the-art collision avoidance systems (CAS). A lot of work is being done to make the CAS as safe and reliable as possible, necessitating a comparative study of the recent work in this important area. The paper provides a comprehensive review of collision avoidance strategies used for unmanned vehicles, with the main emphasis on unmanned aerial vehicles (UAV). It is an in-depth survey of different collision avoidance techniques that are categorically explained along with a comparative analysis of the considered approaches w.r.t. different scenarios and technical aspects. This also includes a discussion on the use of different types of sensors for collision avoidance in the context of UAVs
Intelligent Circuits and Systems
ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society. This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering
Remote Sensing of the Aquatic Environments
The book highlights recent research efforts in the monitoring of aquatic districts with remote sensing observations and proximal sensing technology integrated with laboratory measurements. Optical satellite imagery gathered at spatial resolutions down to few meters has been used for quantitative estimations of harmful algal bloom extent and Chl-a mapping, as well as winds and currents from SAR acquisitions. The knowledge and understanding gained from this book can be used for the sustainable management of bodies of water across our planet
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