Automatic interpretation of salmon scales using deep learning

For several fish species, age and other important biological information is manually inferred from visual scrutinization of scales, and reliable automatic methods are not widely available. Here, we apply Convolutional Neural Networks (CNN) with transfer learning on a novel dataset of 9056 images of Atlantic salmon scales for four different prediction tasks. We predicted fish origin (wild/farmed), spawning history (previous spawner/non-spawner), river age, and sea age. We obtained high prediction accuracy for fish origin (96.70%), spawning history (96.40%), and sea age (86.99%), but lower accuracy for river age (63.20%). Against six human expert readers with an additional dataset of 150 scales, the CNN showed the second-highest percentage agreement for sea age (94.00%, range 87.25±97.30%), but the lowest agreement for river age (66.00%, range 66.00– 84.68%). Estimates of river age by expert readers exhibited higher variance and lower levels of agreement compared to sea age and may indicate why this task is also more difficult for the CNN. Automatic interpretation of scales may provide a cost- and time-efficient method of predicting fish age and life-history traits.publishedVersio

Improving Safety by Learning from Automation in Transport Systems with a Focus on Sensemaking and Meaningful Human Control

Automated transport systems are deployed in many areas and transport modes. The predominant engineering perspective has been to automate as much as possible and minimize human interaction. However, a balanced integration between human factors and technology is often missing, as well as the “hand-over” process between humans and machine. The risks of automated and autonomous systems are emerging, and there is a need to explore how risks can be mitigated through design, focusing on sensemaking, meaningful human control and resilience engineering. This chapter presents key issues from an ongoing research project exploring safety, security and human control of autonomous transport systems in road, sea, rail and air. The chapter aims to answer: (1) What are the major safety and security challenges of autonomous industrial transport systems? (2) What can the various transport modes learn from each other? (3) What are suggested key measures related to organizational, technical and human issues? We have performed literature reviews, interviews and reviewed on-going automation projects. We see the importance of involving humans in the loop during design and operations, support sensemaking, focus on learning from projects through data gathering and risk-based regulation. Unanticipated deviations are key challenges in automated systems, together with how to design for human–automation interaction and meaningful user involvement. Limiting the operational envelope seems to be a key issue for successful implementation and operation of autonomous systems.publishedVersio

DeepOtolith v1.0: An Open-Source AI Platform for Automating Fish Age Reading from Otolith or Scale Images

Every year, marine scientists around the world read thousands of otolith or scale images to determine the age structure of commercial fish stocks. This knowledge is important for fisheries and conservation management. However, the age-reading procedure is time-consuming and costly to perform due to the specialized expertise and labor needed to identify annual growth zones in otoliths. Effective automated systems are needed to increase throughput and reduce cost. DeepOtolith is an open-source artificial intelligence (AI) platform that addresses this issue by providing a web system with a simple interface that automatically estimates fish age by combining otolith images with convolutional neural networks (CNNs), a class of deep neural networks that has been a dominant method in computer vision tasks. Users can upload otolith image data for selective fish species, and the platform returns age estimates. The estimates of multiple images can be exported to conduct conclusions or further age-related research. DeepOtolith currently contains classifiers/regressors for three fish species; however, more species will be included as related work on ageing will be tested and published soon. Herein, the architecture and functionality of the platform are presented. Current limitations and future directions are also discussed. Overall, DeepOtolith should be considered as the first step towards building a community of marine ecologists, machine learning experts, and stakeholders that will collaborate to support the conservation of fishery resources.publishedVersio

Maintenance and barriers: Principles for barrier management in the petroleum industry will be more and more important and It is fundamental to understand the maintenance function in the barrier management.

SummaryThis thesis' sets out to analyse and present the purpose and meaning of safety, maintenance functions and management, maintenance indicators, barrier management and the importance and dependency between those. In addition, this is done in relation to the petroleum industry in Norway, and in collaboration with DNV GL, to develop an improved concept of barrier management. The analyses, discussions and conclusions are done at the basis of earlier accident reports and relevant literature.Safety is the overall process of ensuring a safe environment for every included and associated object. A function that is designed and developed to maintain safety is dependent on frequent maintenance in order to maintain its functionality and capability.One of the four top priorities in the Petroleum Safety Authority Norway's future development program is the improved integration of barriers and barrier management. Barriers are often defined as an obstacle, or a function, to prevent any form of hazardous energy to penetrate at an unwanted area, process or situation. The present situation in the industry indicates deviations in the common understanding and usage of barriers, and there are several areas of potential improvement.In barrier management and the phase of monitoring and improving existing solutions, maintenance has a significant role of making the barriers more relevant, efficient and resilient due to changes, time dependent wear and modifications in the systems. To utilize and control the barriers at the highest level possible, the need for an organised management is required. This includes maintenance, which is an essential process in barrier management of proactively ensure the barriers' continuous improvement and resilience.Maintenance indicators in accordance to barriers could be integrated in barrier management with the purpose of revealing weaknesses and areas of improvement. Backlogging is a maintenance indicator, which reveals if planned and preventive maintenance activities are performed in accordance with the scheduled plan. Backlogging is often measured in days or weeks. Another maintenance indicator is number of errors revealed during maintenance and testing. This indicator measures the ratio of errors in a barrier, after performing a test or maintenance operation. A barrier function often consists of several barrier elements, in which the barrier elements could be both technical, operational and organisational. Thus, maintenance functions and indicators are necessary to maintain and improve the elements. It is important to note, however, that maintenance is not regarded as a direct barrier element in itself.This survey has developed a concept suggestion in cooperation and relation to DNV GL, with the focus at future barrier management guidelines. The emphasis has been on implementing maintenance activities and potential maintenance procedures in the barrier management within areas where earlier/present weaknesses are identified. More specific, a DNV GL concept of internal cooperation and exchange of experience is presented

CAGEREPORTER - Development of technology for autonomous, bio-interactive and high-quality data acquisition from aquaculture net cages

The CageReporter project adapts the use of autonomous and tetherless underwater vehicles as a carrier of sensor systems for data acquisition, where the data are transferred from sea-based fish cages to a centralized land base (Figure 1). The vehicle will use active motion con-trol and acquire data from the cage environment while exploring the fish cages. The main project objective is to develop technology for autonomous functionality for adaptive mission planning to achieve high quality data acquisition from the cage space. One of the most im-portant capabilities within this context is to operate in a dynamically changing environment in interaction with the biomass (bio-interactive) and the aquaculture structures. The project addresses many challenges within the aquaculture industry related to poor accuracy and representative sampling of important variables from the whole volume of the cage. A suc-cessful project outcome will lead to new technology for collection of high-resolution data that could be utilized for assessment of the fish farm state, grouped within three main areas: A) fish, B) aquaculture structures and C) production environment. Examples of areas of applica-tions are detection of abnormal fish behaviour, net inspection and mapping of water quality. CageReporter will provide a solution for continuous 24/7 inspection of the current situation and will be the mobile eyes of the fish farmer in the cage environment. The project idea is based on using low-cost technology for underwater communication, vehicle positioning, and camera systems for 3D vision.publishedVersio

Automatic interpretation of salmon scales using deep learning

For several fish species, age and other important biological information is manually inferred from visual scrutinization of scales, and reliable automatic methods are not widely available. Here, we apply Convolutional Neural Networks (CNN) with transfer learning on a novel dataset of 9056 images of Atlantic salmon scales for four different prediction tasks. We predicted fish origin (wild/farmed), spawning history (previous spawner/non-spawner), river age, and sea age. We obtained high prediction accuracy for fish origin (96.70%), spawning history (96.40%), and sea age (86.99%), but lower accuracy for river age (63.20%). Against six human expert readers with an additional dataset of 150 scales, the CNN showed the second-highest percentage agreement for sea age (94.00%, range 87.25±97.30%), but the lowest agreement for river age (66.00%, range 66.00– 84.68%). Estimates of river age by expert readers exhibited higher variance and lower levels of agreement compared to sea age and may indicate why this task is also more difficult for the CNN. Automatic interpretation of scales may provide a cost- and time-efficient method of predicting fish age and life-history traits

Automatic interpretation of otoliths using deep learning

The age structure of a fish population has important implications for recruitment processes and population fluctuations, and is a key input to fisheries-assessment models. The current method of determining age structure relies on manually reading age from otoliths, and the process is labor intensive and dependent on specialist expertise. Recent advances in machine learning have provided methods that have been remarkably successful in a variety of settings, with potential to automate analysis that previously required manual curation. Machine learning models have previously been successfully applied to object recognition and similar image analysis tasks. Here we investigate whether deep learning models can also be used for estimating the age of otoliths from images. We adapt a pre-trained convolutional neural network designed for object recognition, to estimate the age of fish from otolith images. The model is trained and validated on a large collection of images of Greenland halibut otoliths. We show that the model works well, and that its precision is comparable to documented precision obtained by human experts. Automating this analysis may help to improve consistency, lower cost, and increase the extent of age estimation. Given that adequate data are available, this method could also be used to estimate age of other species using images of otoliths or fish scales

Automatic interpretation of otoliths using deep learning

The age structure of a fish population has important implications for recruitment processes and population fluctuations, and is a key input to fisheries-assessment models. The current method of determining age structure relies on manually reading age from otoliths, and the process is labor intensive and dependent on specialist expertise. Recent advances in machine learning have provided methods that have been remarkably successful in a variety of settings, with potential to automate analysis that previously required manual curation. Machine learning models have previously been successfully applied to object recognition and similar image analysis tasks. Here we investigate whether deep learning models can also be used for estimating the age of otoliths from images. We adapt a pre-trained convolutional neural network designed for object recognition, to estimate the age of fish from otolith images. The model is trained and validated on a large collection of images of Greenland halibut otoliths. We show that the model works well, and that its precision is comparable to documented precision obtained by human experts. Automating this analysis may help to improve consistency, lower cost, and increase the extent of age estimation. Given that adequate data are available, this method could also be used to estimate age of other species using images of otoliths or fish scales.publishedVersio

Improving Safety by Learning from Automation in Transport Systems with a Focus on Sensemaking and Meaningful Human Control

Automated transport systems are deployed in many areas and transport modes. The predominant engineering perspective has been to automate as much as possible and minimize human interaction. However, a balanced integration between human factors and technology is often missing, as well as the “hand-over” process between humans and machine. The risks of automated and autonomous systems are emerging, and there is a need to explore how risks can be mitigated through design, focusing on sensemaking, meaningful human control and resilience engineering. This chapter presents key issues from an ongoing research project exploring safety, security and human control of autonomous transport systems in road, sea, rail and air. The chapter aims to answer: (1) What are the major safety and security challenges of autonomous industrial transport systems? (2) What can the various transport modes learn from each other? (3) What are suggested key measures related to organizational, technical and human issues? We have performed literature reviews, interviews and reviewed on-going automation projects. We see the importance of involving humans in the loop during design and operations, support sensemaking, focus on learning from projects through data gathering and risk-based regulation. Unanticipated deviations are key challenges in automated systems, together with how to design for human–automation interaction and meaningful user involvement. Limiting the operational envelope seems to be a key issue for successful implementation and operation of autonomous systems

Improving Safety by Learning from Automation in Transport Systems with a Focus on Sensemaking and Meaningful Human Control

Automated transport systems are deployed in many areas and transport modes. The predominant engineering perspective has been to automate as much as possible and minimize human interaction. However, a balanced integration between human factors and technology is often missing, as well as the “hand-over” process between humans and machine. The risks of automated and autonomous systems are emerging, and there is a need to explore how risks can be mitigated through design, focusing on sensemaking, meaningful human control and resilience engineering. This chapter presents key issues from an ongoing research project exploring safety, security and human control of autonomous transport systems in road, sea, rail and air. The chapter aims to answer: (1) What are the major safety and security challenges of autonomous industrial transport systems? (2) What can the various transport modes learn from each other? (3) What are suggested key measures related to organizational, technical and human issues? We have performed literature reviews, interviews and reviewed on-going automation projects. We see the importance of involving humans in the loop during design and operations, support sensemaking, focus on learning from projects through data gathering and risk-based regulation. Unanticipated deviations are key challenges in automated systems, together with how to design for human–automation interaction and meaningful user involvement. Limiting the operational envelope seems to be a key issue for successful implementation and operation of autonomous systems