Development of a Simulator for Prototyping Reinforcement Learning based Autonomous Cars

Autonomous driving is a research field that has received attention in recent years, with increasing applications of reinforcement learning (RL) algorithms. It is impractical to train an autonomous vehicle thoroughly in the physical space, i.e., the so-called ’real world’; therefore, simulators are used in almost all training of autonomous driving algorithms. There are numerous autonomous driving simulators, very few of which are specifically targeted at RL. RL-based cars are challenging due to the variety of reward functions available. There is a lack of simulators addressing many central RL research tasks within autonomous driving, such as scene understanding, localization and mapping, planning and driving policies, and control, which have diverse requirements and goals. It is, therefore, challenging to prototype new RL projects with different simulators, especially when there is a need to examine several reward functions at once. This paper introduces a modified simulator based on the Udacity simulator, made for autonomous cars using RL. It creates reward functions, along with sensors to create a baseline implementation for RL-based vehicles. The modified simulator also resets the vehicle when it gets stuck or is in a non-terminating loop, making it more reliable. Overall, the paper seeks to make the prototyping of new systems simple, with the testing of different RL-based systems.Development of a Simulator for Prototyping Reinforcement Learning based Autonomous CarspublishedVersio

Accurate Wound and Lice Detection in Atlantic Salmon Fish Using a Convolutional Neural Network

The population living in the coastal region relies heavily on fish as a food source due to their vast availability and low cost. This need has given rise to fish farming. Fish farmers and the fishing industry face serious challenges such as lice in the aquaculture ecosystem, wounds due to injuries, early fish maturity, etc. causing millions of fish deaths in the fish aquaculture ecosystem. Several measures, such as cleaner fish and anti-parasite drugs, are utilized to reduce sea lice, but getting rid of them entirely is challenging. This study proposed an image-based machine-learning technique to detect wounds and the presence of lice in the live salmon fish farm ecosystem. A new equally distributed dataset contains fish affected by lice and wounds and healthy fish collected from the fish tanks installed at the Institute of Marine Research, Bergen, Norway. A convolutional neural network is proposed for fish lice and wound detection consisting of 15 convolutional and 5 dense layers. The proposed methodology has a test accuracy of 96.7% compared with established VGG-19 and VGG-16 models, with accuracies of 91.2% and 92.8%, respectively. The model has a low false and true positive rate of 0.011 and 0.956, and 0.0307 and 0.965 for fish having lice and wounds, respectively.publishedVersio

Towards Using Reinforcement Learning for Autonomous Docking of Unmanned Surface Vehicles

Author's accepted manuscriptProviding full autonomy to Unmanned Surface Vehicles (USV) is a challenging goal to achieve. Autonomous docking is a subtask that is particularly difficult. The vessel has to distinguish between obstacles and the dock, and the obstacles can be either static or moving. This paper developed a simulator using Reinforcement Learning (RL) to approach the problem. We studied several scenarios for the task of docking a USV in a simulator environment. The scenarios were defined with different sensor inputs and start-stop procedures but a simple shared reward function. The results show that the system solved the task when the IMU (Inertial Measurement Unit) and GNSS (Global Navigation Satellite System) sensors were used to estimate the state, despite the simplicity of the reward function.acceptedVersio

Temperate fish detection and classification: a deep learning based approach

A wide range of applications in marine ecology extensively uses underwater cameras. Still, to efficiently process the vast amount of data generated, we need to develop tools that can automatically detect and recognize species captured on film. Classifying fish species from videos and images in natural environments can be challenging because of noise and variation in illumination and the surrounding habitat. In this paper, we propose a two-step deep learning approach for the detection and classification of temperate fishes without pre-filtering. The first step is to detect each single fish in an image, independent of species and sex. For this purpose, we employ the You Only Look Once (YOLO) object detection technique. In the second step, we adopt a Convolutional Neural Network (CNN) with the Squeeze-and-Excitation (SE) architecture for classifying each fish in the image without pre-filtering. We apply transfer learning to overcome the limited training samples of temperate fishes and to improve the accuracy of the classification. This is done by training the object detection model with ImageNet and the fish classifier via a public dataset (Fish4Knowledge), whereupon both the object detection and classifier are updated with temperate fishes of interest. The weights obtained from pre-training are applied to post-training as a priori. Our solution achieves the state-of-the-art accuracy of 99.27% using the pre-training model. The accuracies using the post-training model are also high; 83.68% and 87.74% with and without image augmentation, respectively. This strongly indicates that the solution is viable with a more extensive dataset.publishedVersio

Branch-Manoeuvring Capable Pipe Cleaning Robot for Aquaponic Systems

Available from 23.07.2023.Aquaponic systems are engineered ecosystems combining aquaculture and plant production. Nutrient rich water is continuously circulating through the system from aquaculture tanks. A biofilter with nitrifying bacteria breaks down fish metabolism ammonia into nitrite and nitrate, which plants and makes the aquaculture wastewater into valued organic fertiliser for the plants, containing essential macro and micro elements. At the same time, the plants are cleaning the water by absorbing ammonia from the fish tanks before it reaches dangerous levels for the aquatic animals. In principle, the only external input is energy, mainly in the form of light and heat, but fish food is also commonly provided. Growing fish food is potentially feasible in a closed loop system, hence aquaponic systems can possibly be an important source of proteins and other important nutrition when, for example, colonising other planets in the future. Fully autonomous aquaponic systems are currently not available. This work aims at minimising manual labour related to cleaning pipes for water transport. The cleaning process must be friendly to both plants and aquatic animals. Hence, in this work, pure mechanical cleaning is adopted. A novel belt-driven continuum robot capable of travelling through small/medium diameter pipes and manoeuvring branches and bends, is designed and tested. The robot is modular and can be extended with different cleaning modules through an interface providing CAN-bus network and electric power. The flexible continuum modules of the robot are characterised. Experimental results demonstrate that the robot is able to travel through pipes with diameters varying from 50 mm to 75 mm, and also capable of handling T-branches of up to 90∘.acceptedVersionPaid Open Acces

Biometric Fish Classification of Temperate Species Using Convolutional Neural Network with Squeeze-and-Excitation

submittedVersio

Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook

The deep learning (DL) revolution is touching all scientific disciplines and corners of our lives as a means of harnessing the power of big data. Marine ecology is no exception. New methods provide analysis of data from sensors, cameras, and acoustic recorders, even in real time, in ways that are reproducible and rapid. Off-the-shelf algorithms find, count, and classify species from digital images or video and detect cryptic patterns in noisy data. These endeavours require collaboration across ecological and data science disciplines, which can be challenging to initiate. To promote the use of DL towards ecosystem-based management of the sea, this paper aims to bridge the gap between marine ecologists and computer scientists. We provide insight into popular DL approaches for ecological data analysis, focusing on supervised learning techniques with deep neural networks, and illustrate challenges and opportunities through established and emerging applications of DL to marine ecology. We present case studies on plankton, fish, marine mammals, pollution, and nutrient cycling that involve object detection, classification, tracking, and segmentation of visualized data. We conclude with a broad outlook of the field’s opportunities and challenges, including potential technological advances and issues with managing complex data sets.publishedVersionPaid Open Acces

Unlocking the potential of deep learning for marine ecology: overview, applications, and outlook

publishedVersio

Development of a Simulator for Prototyping Reinforcement Learning based Autonomous Cars

Autonomous driving is a research field that has received attention in recent years, with increasing applications of reinforcement learning (RL) algorithms. It is impractical to train an autonomous vehicle thoroughly in the physical space, i.e., the so-called ’real world’; therefore, simulators are used in almost all training of autonomous driving algorithms. There are numerous autonomous driving simulators, very few of which are specifically targeted at RL. RL-based cars are challenging due to the variety of reward functions available. There is a lack of simulators addressing many central RL research tasks within autonomous driving, such as scene understanding, localization and mapping, planning and driving policies, and control, which have diverse requirements and goals. It is, therefore, challenging to prototype new RL projects with different simulators, especially when there is a need to examine several reward functions at once. This paper introduces a modified simulator based on the Udacity simulator, made for autonomous cars using RL. It creates reward functions, along with sensors to create a baseline implementation for RL-based vehicles. The modified simulator also resets the vehicle when it gets stuck or is in a non-terminating loop, making it more reliable. Overall, the paper seeks to make the prototyping of new systems simple, with the testing of different RL-based systems