The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024

The 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 addresses maritime computer vision for Unmanned Aerial Vehicles (UAV) and Unmanned Surface Vehicles (USV). Three challenges categories are considered: (i) UAV-based Maritime Object Tracking with Re-identification, (ii) USV-based Maritime Obstacle Segmentation and Detection, (iii) USV-based Maritime Boat Tracking. The USV-based Maritime Obstacle Segmentation and Detection features three sub-challenges, including a new embedded challenge addressing efficicent inference on real-world embedded devices. This report offers a comprehensive overview of the findings from the challenges. We provide both statistical and qualitative analyses, evaluating trends from over 195 submissions. All datasets, evaluation code, and the leaderboard are available to the public at https://macvi.org/workshop/macvi24.Comment: Part of 2nd Workshop on Maritime Computer Vision (MaCVi) 2024 IEEE Xplore submission as part of WACV 202

Planiranje putanje autonomnoga plovila zasnivano na sonarskim podacima u svrhu potpunoga prekrivanja velikih površina morskoga dna

Efficient mapping of an unknown large-scale marine area using a side-scan sonar onboard an autonomous marine vehicle is often of great importance. It might also be important to scan parts of an area in more detail and from more than one side. In contrast to the standard offline static coverage problem solution for side-scan sonar missions based on the overlapping all across-track sonar swaths in a lawnmower pattern, several online sonar-data driven coverage path planning algorithms are proposed in this thesis. The proposed algorithms provide a coverage solution based on local information gain computed from the side-scan sonar data during the mission execution, which is then exploited for replanning. In addition, the proposed solution takes into account the coverage path length/time to ensure equal information content about arbitrarily defined interesting objects as in case of the standard lawnmower with much less resources needed to complete the same mission. One dynamical programming-based and three decision making-based coverage path (re)planning algorithms are proposed herein. Their performance was thoroughly examined in a grid-like coverage area cost map simulation over a whole range of coverage mission parameters and randomly generated seafloor configurations to gain statistical metrics of their behavior. Also, their upper and lower performance bounds are mathematically modelled w.r.t. some mission parameters, and were validated by the thousands of simulation runs. Moreover, an existing realistic 3D simulation environment was extended by the above-mentioned coverage path planners, mission controller, and sonar data processing modules to gain further insights into the interplay of coverage path planning, feasible path interpolation, vehicle dynamics and control.Učinkovito mapiranje velikih nepoznatih područja morskoga dna korištenjem bočno skenirajućeg sonara (engl. side-scan sonar) i autonomnoga pomorskog vozila je često od velikog značaja. Također, često je bitno detaljnije snimiti zanimljive dijelove tog područja i to s više od jedne strane. Predloženo je nekoliko online metoda prekrivanja područja zasnovanih na sonarskim podacima. Razvijene metode su poređene sa standardnim offline rješenjem problema prekrivanja sonarom, tzv. uzorkom kosilice koji sve objekte neadaptivno snima sa obje strane bez obzira da li uopće postoje ikakvi objekti na morskome dnu, i ako postoje da li su zanimljivi za trenutnu misiju. Za razliku od toga, predloženi algoritmi nude rješenje problema (re)planiranja prekrivanja područja zasnovano na sonarskim podacima u tijeku misije. Nadalje, oni u dosta velikom području vrijednosti parametara misije nude jednako informativnu snimku interesantnih objekata kao i neadaptivni pristup uz znatno skraćenje trajanja misije/dužine putanje prekrivanja. Jedan algoritam zasnovan na dinamičkom programiranju i tri algoritma zasnovana na heurističkom donošenju odluka su predloženi u ovom radu. Njihove performanse su detaljno ispitane na matričnoj mapi cijena prekrivanja i to za širok opseg vrijednosti parametara misije i slučajno generarirane konfiguracije morskoga dna kako bi se dobio uvid u statistiku ponašanja navedenih algoritama. Također, njihove gornje i donje granice performansi su matematički opisane u ovisnosti od parametara misije, te su validirane tisućama provedenih simulacija. Štoviše, postojeće realistično 3D simulacijsko okruženje je prošireno sa gore navedenim planerima prekrivanja, kontrolerom misije, i modulom obrade sonarske slike kako bi se stekao još i bolji uvid u međusobne utjecaje modula planiranja putanje prekrivanja, interpolacije izvedivih putanja, te dinamike i upravljanja vozilom

Planiranje putanje autonomnoga plovila zasnivano na sonarskim podacima u svrhu potpunoga prekrivanja velikih površina morskoga dna

Efficient mapping of an unknown large-scale marine area using a side-scan sonar onboard an autonomous marine vehicle is often of great importance. It might also be important to scan parts of an area in more detail and from more than one side. In contrast to the standard offline static coverage problem solution for side-scan sonar missions based on the overlapping all across-track sonar swaths in a lawnmower pattern, several online sonar-data driven coverage path planning algorithms are proposed in this thesis. The proposed algorithms provide a coverage solution based on local information gain computed from the side-scan sonar data during the mission execution, which is then exploited for replanning. In addition, the proposed solution takes into account the coverage path length/time to ensure equal information content about arbitrarily defined interesting objects as in case of the standard lawnmower with much less resources needed to complete the same mission. One dynamical programming-based and three decision making-based coverage path (re)planning algorithms are proposed herein. Their performance was thoroughly examined in a grid-like coverage area cost map simulation over a whole range of coverage mission parameters and randomly generated seafloor configurations to gain statistical metrics of their behavior. Also, their upper and lower performance bounds are mathematically modelled w.r.t. some mission parameters, and were validated by the thousands of simulation runs. Moreover, an existing realistic 3D simulation environment was extended by the above-mentioned coverage path planners, mission controller, and sonar data processing modules to gain further insights into the interplay of coverage path planning, feasible path interpolation, vehicle dynamics and control.Učinkovito mapiranje velikih nepoznatih područja morskoga dna korištenjem bočno skenirajućeg sonara (engl. side-scan sonar) i autonomnoga pomorskog vozila je često od velikog značaja. Također, često je bitno detaljnije snimiti zanimljive dijelove tog područja i to s više od jedne strane. Predloženo je nekoliko online metoda prekrivanja područja zasnovanih na sonarskim podacima. Razvijene metode su poređene sa standardnim offline rješenjem problema prekrivanja sonarom, tzv. uzorkom kosilice koji sve objekte neadaptivno snima sa obje strane bez obzira da li uopće postoje ikakvi objekti na morskome dnu, i ako postoje da li su zanimljivi za trenutnu misiju. Za razliku od toga, predloženi algoritmi nude rješenje problema (re)planiranja prekrivanja područja zasnovano na sonarskim podacima u tijeku misije. Nadalje, oni u dosta velikom području vrijednosti parametara misije nude jednako informativnu snimku interesantnih objekata kao i neadaptivni pristup uz znatno skraćenje trajanja misije/dužine putanje prekrivanja. Jedan algoritam zasnovan na dinamičkom programiranju i tri algoritma zasnovana na heurističkom donošenju odluka su predloženi u ovom radu. Njihove performanse su detaljno ispitane na matričnoj mapi cijena prekrivanja i to za širok opseg vrijednosti parametara misije i slučajno generarirane konfiguracije morskoga dna kako bi se dobio uvid u statistiku ponašanja navedenih algoritama. Također, njihove gornje i donje granice performansi su matematički opisane u ovisnosti od parametara misije, te su validirane tisućama provedenih simulacija. Štoviše, postojeće realistično 3D simulacijsko okruženje je prošireno sa gore navedenim planerima prekrivanja, kontrolerom misije, i modulom obrade sonarske slike kako bi se stekao još i bolji uvid u međusobne utjecaje modula planiranja putanje prekrivanja, interpolacije izvedivih putanja, te dinamike i upravljanja vozilom

A Visual Servoing Scheme for Autonomous Aquaculture Net Pens Inspection Using ROV

Aquaculture net pens inspection and monitoring are important to ensure net stability and fish health in the fish farms. Remotely operated vehicles (ROVs) offer a low-cost and sophisticated solution for the regular inspection of the underwater fish net pens due to their ability of visual sensing and autonomy in a challenging and dynamic aquaculture environment. In this paper, we report the integration of an ROV with a visual servoing scheme for regular inspection and tracking of the net pens. We propose a vision-based positioning scheme that consists of an object detector, a pose generator, and a closed-loop controller. The system employs a modular approach that first utilizes two easily identifiable parallel ropes attached to the net for image processing through traditional computer vision methods. Second, the reference positions of the ROV relative to the net plane are extracted on the basis of a vision triangulation method. Third, a closed-loop control law is employed to instruct the vehicle to traverse from top to bottom along the net plane to inspect its status. The proposed vision-based scheme has been implemented and tested both through simulations and field experiments. The extensive experimental results have allowed the assessment of the performance of the scheme that resulted satisfactorily and can supplement the traditional aquaculture net pens inspection and tracking systems

Marine Robots Mapping the Present and the Past: Unraveling the Secrets of the Deep

Underwater cultural heritage sites are subject to constant change, whether due to natural forces such as sediments, waves, currents or human intervention. Until a few decades ago, the documentation and research of these sites was mostly done manually by diving archaeologists. This paper presents the results of the integration of remote sensing technologies with autonomous marine vehicles in order to make the task of site documentation even faster, more accurate, more efficient and more precisely georeferenced. It includes the integration of multibeam sonar, side scan sonar and various cameras into autonomous surface and underwater vehicles, remotely operated vehicle and unmanned aerial vehicle. In total, case studies for nine underwater cultural heritage sites around the Mediterranean region are presented. Each case study contains a brief archaeological background of the site, the methodology of using autonomous marine vehicles and sensors for their documentation, and the results in the form of georeferenced side-scan sonar mosaics, bathymetric models or reconstructed photogrammetric models. It is important to mention that this was the first time that any of the selected sites were documented with sonar technologies or autonomous marine vehicles. The main objective of these surveys was to document and assess the current state of the sites and to establish a basis on which future monitoring operations could be built and compared. Beyond the mere documentation and physical preservation, examples of the use of these results for the digital preservation of the sites in augmented and virtual reality are presented

Heterogeneous Autonomous Robotic System in Viticulture and Mariculture: Vehicles Development and Systems Integration

There are activities in viticulture and mariculture that require extreme physical endurance from human workers, making them prime candidates for automation and robotization. This paper presents a novel, practical, heterogeneous, autonomous robotic system divided into two main parts, each dealing with respective scenarios in viticulture and mariculture. The robotic components and the subsystems that enable collaboration were developed as part of the ongoing HEKTOR project, and each specific scenario is presented. In viticulture, this includes vineyard surveillance, spraying and suckering with an all-terrain mobile manipulator (ATMM) and a lightweight autonomous aerial robot (LAAR) that can be used in very steep vineyards where other mechanization fails. In mariculture, scenarios include coordinated aerial and subsurface monitoring of fish net pens using the LAAR, an autonomous surface vehicle (ASV), and a remotely operated underwater vehicle (ROV). All robotic components communicate and coordinate their actions through the Robot Operating System (ROS). Field tests demonstrate the great capabilities of the HEKTOR system for the fully autonomous execution of very strenuous and hazardous work in viticulture and mariculture, while meeting the necessary conditions for the required quality and quantity of the work performed