A Communication Interface for Multilayer Cloud Computing Architecture for Low Cost Underwater Vehicles

To enable high computational loads for low cost underwater drones, a cloud based architecture is proposed to take advantage of recent development in machine learning and computer vision. The processing power made available will benefit vehicles with limited onboard processing capacity. The rapid development of cloud computing services have made servers with significant computational resources easier to access. In this paper, a communication interface for cloud based multilayer architecture is proposed to enable real time performance by distributing the workload to networked processing devices. It adopts a publish-subscribe model for efficient communication between the layers. The latency and workload distribution are evaluated to assess the efficiency of the proposed method. An application to semantic segmentation of under-water scenes is also tested to measure the framework capabilities for real-time operation using more resource-demanding tools. The conducted experiments resulted in time and performance gains through offloading the underwater vehicle, and forwarding the computations to the cloud based layer

Applications of geo-referenced underwater photo mosaics in marine biology and archaeology

Author Posting. © Oceanography Society, 2007. This article is posted here by permission of Oceanography Society for personal use, not for redistribution. The definitive version was published in Oceanography 20, 4 (2007): 140-149.In deep water, below the photic zone, still and video imaging of the seabed requires artificial lighting. Light absorption and backscatter caused by typical seawater components, such as dissolved organic matter, plankton, and inorganic particles, often limit the artificially lit area to a few square meters. To obtain high-resolution photographic data of larger seabed areas, a series of images can be compiled into a photo mosaic. Image mosaics are easier to interpret, communicate, and exhibit than video footage or a series of images, because the individual image frames in a photo mosaic are naturally represented in a spatial context

Semantic Segmentation in Underwater Ship Inspections: Benchmark and Dataset

In this article, we present the first large-scale data set for underwater ship lifecycle inspection, analysis and condition information (LIACI). It contains 1893 images with pixel annotations for ten object categories: defects, corrosion, paint peel, marine growth, sea chest gratings, overboard valves, propeller, anodes, bilge keel and ship hull. The images have been collected during underwater ship inspections and annotated by human domain experts. We also present a benchmark evaluation of state-of-the-art semantic segmentation approaches based on standard performance metrics. Consequently, we propose to use U-Net with a MobileNetV2 backbone for the segmentation task due to its balanced tradeoff between performance and computational efficiency, which is essential if used for real-time evaluation. Also, we demonstrate its benefits for in-water inspections by providing quantitative evaluations of the inspection findings. With a variety of use cases, the proposed segmentation pipeline and the LIACI data set create new promising opportunities for future research in underwater ship inspections.publishedVersio

Semantic Segmentation in Underwater Ship Inspections: Benchmark and Dataset

In this article, we present the first large-scale data set for underwater ship lifecycle inspection, analysis and condition information (LIACI). It contains 1893 images with pixel annotations for ten object categories: defects, corrosion, paint peel, marine growth, sea chest gratings, overboard valves, propeller, anodes, bilge keel and ship hull. The images have been collected during underwater ship inspections and annotated by human domain experts. We also present a benchmark evaluation of state-of-the-art semantic segmentation approaches based on standard performance metrics. Consequently, we propose to use U-Net with a MobileNetV2 backbone for the segmentation task due to its balanced tradeoff between performance and computational efficiency, which is essential if used for real-time evaluation. Also, we demonstrate its benefits for in-water inspections by providing quantitative evaluations of the inspection findings. With a variety of use cases, the proposed segmentation pipeline and the LIACI data set create new promising opportunities for future research in underwater ship inspections

Multi-label Video Classification for Underwater Ship Inspection

Today ship hull inspection including the examination of the external coating, detection of defects, and other types of external degradation such as corrosion and marine growth is conducted underwater by means of Remotely Operated Vehicles (ROVs). The inspection process consists of a manual video analysis which is a time-consuming and labor-intensive process. To address this, we propose an automatic video analysis system using deep learning and computer vision to improve upon existing methods that only consider spatial information on individual frames in underwater ship hull video inspection. By exploring the benefits of adding temporal information and analyzing frame-based classifiers, we propose a multi-label video classification model that exploits the self-attention mechanism of transformers to capture spatiotemporal attention in consecutive video frames. Our proposed method has demonstrated promising results and can serve as a benchmark for future research and development in underwater video inspection applications.Comment: Accepted to be presented at OCEANS 2023 Limerick conference and will be published by IEE

Trent til å lykkes når bjella ringer

Master's thesis in Risk management and societal safetyBeredskapsplanen har til hensikt å være et hjelpemiddel for ulike aktører når det oppstår en uønsket hendelse av en viss skala. Beredskapsplanen er forankret i lovverk, og det foreligger forskning som beskriver hvordan planen bør være bygget opp og hva den bør inneholde. Planen bør øves for å kontrollere og teste om den har en praktisk verdi. Det bør derfor planlegges og tilrettelegges for øvelser som har til hensikt å avdekke feil og mangler ved beredskapsplanen, samt vurdere hvilke elementer som virker etter sin hensikt. Det finnes mange øvingsformer. Disse gjennomføres på ulike måter og danner ulike læringsgrunnlag. Det bør derfor være en bevisst tilnærming til hvilken øvingsform som benyttes, og dette må samsvare med hvilke elementer av beredskapsplanen som skal øves og i hvilken grad hele eller deler av planen skal øves. For å sikre læring i etterkant av øvelser som kan bidra til at beredskapsplanen utbedres og videreutvikles, må det gjennomføres evaluering av øvelsene. Denne oppgaven søker å ta for seg tematikken som er beskrevet ved å stille følgende problemstilling: Hvordan kan øvelser bidra til en kvalitetssikring og utvikling av beredskapsplanen? Studiet har avdekket følgende funn: * Det må foreligge en systematikk i hvordan læring oppnås ved trening og øving. Øvingen må være aktualisert opp mot beredskapsplanen. Evaluering må ha en systematisk tilnærming i alle øvelsens faser. Øvelse og evaluering må være tilstrekkelig forankret i ledelsesleddet. * Varslede øvelser er generelt foretrukket fremfor ikke-varslede øvelser. Det kan derimot være hensiktsmessig å implementere ikke-varslede momenter i varslede øvelser. Det må allikevel være en bevissthet tilknyttet bruken av ikke-varslede elementer i øvelser da det foreligger risiko for at dette kan lede til manglende mestringstro og motivasjon blant øvingsdeltakere. * Beredskapsplanen bør kartlegge og beskrive tilstøtende aktører, og det bør foreligge en overordnet plan som er tilgjengelig for samvirkeaktører. Øvelser kan bidra til å tilføre tverrsektoriell kultur og læring til planverket

Observations of Turbulence at a Near-Surface Temperature Front in the Arctic Ocean

High-resolution ocean temperature, salinity, current, and turbulence data were collected at an Arctic thermohaline front in the Nansen Basin. The front was close to the sea ice edge and separated the cold and fresh surface melt water from the warm and saline mixed layer. Measurements were made on 18 September 2018, in the upper 100 m, from a research vessel and an autonomous underwater vehicle. Destabilizing surface buoyancy fluxes from a combination of heat loss to the atmosphere and cross-front Ekman transport by down-front winds reduced the potential vorticity in the upper ocean. Turbulence structure in the mixed layer was generally consistent with turbulence production through convection by heat loss to atmosphere and mechanical forcing by moderate winds. Conditions at the front were favorable for forced symmetric instability, a mechanism drawing energy from the frontal geostrophic current. A clear signature of increased dissipation from symmetric instability could not be identified; however, this instability could potentially account for the increased dissipation rates at the front location down to 40 m depth that could not be explained by the atmospheric forcing. This turbulence was associated with turbulent heat fluxes of up to 10 W m−2, eroding the warm and cold intrusions observed between 30 and 60 m depth. A Seaglider sampled across a similar frontal structure in the same region 10 days after our survey. The submesoscale-to-turbulence-scale transitions and resulting mixing can be widespread and important in the Atlantic sector of the Arctic Ocean.publishedVersio

A flow-through imaging system for automated measurement of ichthyoplankton

Microscopic imaging and morphometric measurement of fish embryos and larvae is essential in environmental monitoring of fish populations and to evaluate larvae development in aquaculture. Traditional microscopy methods require time-consuming, repetitive work by human experts. We present a method for fast imaging and analysis of millimetre-scale ichthyoplankton suspended in seawater. Our system can be easily built from common and off-the-shelf components and uses open-source software for image capture and analysis. Our system obtains images of similar quality to traditional microscopy, and biological measurements comparable to those by human experts, with minimal human interaction. This saves time and effort, while increasing the size of data sets obtained. We demonstrate our approach with cod eggs and larvae, and present results showing biologically relevant endpoints including egg diameter, larval standard length, yolk volume and eye diameter, with comparison to similar measurements reported in the literature. • High throughput, microscope-scale imaging of fish eggs and larvae • Automated measurement of biologically relevant endpoints • Easily built from off-the-shelf components and open-source softwarepublishedVersio