Query: surface vessels deep learning

title: Towards automated identification of ice features for surface vessels using deep learning

title: Deep reinforcement learning based path planning of underactuated surface vessels
abstract: This paper is dedicated to the issues of path planning for the underactuated surface vessel (USV) in unknown environments with obstacles. Aiming at the usability problem caused by the complicated control law of the traditional method, a deep deterministic policy gradient (DDPG) based path planning algorithm is proposed with the powerful actor critic architecture. The main contributions of this paper are as follows: (1) The DDPG based path planning method is the decision control system of USV whose output is continuous. (2) A complete reward system is specially designed for target approaching, speed control and attitude correction. (3) Since the reward system is independent, it is highly customisable and can be changed according to the task and the control model. Through the simulation, the results show that the perfect path can be automatically generated under unknown environmental disturbance, thus providing the proposed DDPG based path planning scheme effectiveness and applied meaning.

title: Deep Learning Based on Striation Images for Underwater and Surface Target Classification
abstract: Currently, synthetic aperture radar (SAR) images are generally used for ship identification. However, the SAR can only be used to classify surface ships without any underwater target. In contrast, since sonar can receive radiated noises from both vessels and underwater targets, its images can be used to effectively identify targets at different depths. However, due to the shortage of underwater target data and the difficulty in modeling, sonar images are hardly used for deep learning (DL) To solve such problems, this letter proposes a compound convolutional neural network (CSDN) based on a shared latent sparse feature (SLS) and a deep belief network (DBN) to learn striation based sonar images. This letter has three contributions. First, this letter uses striation images to overcome the lack of training data for CNNs. Second, the DBN is applied to optimize fuzzy or discontinuous fringes, and an SLS feature is proposed to represent the interference fringe. Finally, the two features are exploited to separately train CNNs, combined with weight, to enhance the accuracy of classifying water targets. The experimental results indicate that, compared with the other DL based models VGG, SSD, RFCN, and SCDAE a CSDN is more stable for different datasets and has the highest accuracy of up to 93.34%

title: Segmentation of Pelvic Vessels in Pediatric MRI Using a Patch Based Deep Learning Approach
abstract: In this paper, we propose a patch based deep learning approach to segment pelvic vessels in 3D MRI images of pediatric patients. For a given T2 weighted MRI volume, a set of 2D axial patches are extracted using a limited number of user selected landmarks. In order to take into account the volumetric information, successive 2D axial patches are combined together, producing a set of pseudo RGB color images. These RGB images are then used as input for a convolutional neural network (CNN) pre trained on the ImageNet dataset, which results into both segmentation and vessel labeling as veins or arteries. The proposed method is evaluated on 35 MRI volumes of pediatric patients, obtaining an average segmentation accuracy in terms of Average Symmetric Surface Distance of (ASSD 0.89 \pm 0.07\ mm and Dice Index of (DC 0.79 \pm

title: Using Deep Reinforcement Learning Methods for Autonomous Vessels in 2D Environments
abstract: Unmanned Surface Vehicles technology (USVs) is an exciting topic that essentially deploys an algorithm to safely and efficiently performs a mission. Although reinforcement learning is a well known approach to modeling such a task, instability and divergence may occur when combining off policy and function approximation. In this work, we used deep reinforcement learning combining Q learning with a neural representation to avoid instability. Our methodology uses deep q learning and combines it with a rolling wave planning approach on agile methodology. Our method contains two critical parts in order to perform missions in an unknown environment. The first is a path planner that is responsible for generating a potential effective path to a destination without considering the details of the root. The latter is a decision making module that is responsible for short term decisions on avoiding obstacles during the near future steps of USV exploitation within the context of the value function. Simulations were performed using two algorithms: a basic vanilla vessel navigator (VVN) as a baseline and an improved one for the vessel navigator with a planner and local view (VNPLV) Experimental results show that the proposed method enhanced the performance of VVN by 55.31% on average for long distance missions. Our model successfully demonstrated obstacle avoidance by means of deep reinforcement learning using planning adaptive paths in unknown environments.

title: COLREG Compliant Collision Avoidance for Unmanned Surface Vehicle Using Deep Reinforcement Learning
abstract: Path Following and Collision Avoidance, be it for unmanned surface vessels or other autonomous vehicles, are two fundamental guidance problems in robotics. For many decades, they have been subject to academic study, leading to a vast number of proposed approaches. However, they have mostly been treated as separate problems, and have typically relied on non linear first principles models with parameters that can only be determined experimentally. The rise of deep reinforcement learning in recent years suggests an alternative approach: end to end learning of the optimal guidance policy from scratch by means of a trial and error based approach. In this article, we explore the potential of Proximal Policy Optimization, a deep reinforcement learning algorithm with demonstrated state of the art performance on Continuous Control tasks, when applied to the dual objective problem of controlling an autonomous surface vehicle in a COLREGs compliant manner such that it follows an a priori known desired path while avoiding collisions with other vessels along the way. Based on high fidelity elevation and AIS tracking data from the Trondheim Fjord, an inlet of the Norwegian sea, we evaluate the trained agent's performance in challenging, dynamic real world scenarios where the ultimate success of the agent rests upon its ability to navigate non uniform marine terrain while handling challenging, but realistic vessel encounters.

title: Fuzzy Categorical Deep Reinforcement Learning of a Defensive Game for an Unmanned Surface Vessel
abstract: Unmanned surface vessels (USVs) have great significance and wide applications in many fields, whereas the control law designed with the analytical approach is too complicated to implement, subject to the level of hardware development. Confronted with obstacles, USVs use the conventional method to avoid them, but in many practical cases, it is difficult to devise the path in advance. Moreover, prior knowledge, including expert experience, may be challenging to introduce into a control system effectively. In this paper, a fuzzy categorical deep reinforcement learning based framework is established to handle a sophisticated obstruction situation. The framework consists of an interactive observation module and a control module with fuzzy reward shaping. Experimental results verify that the performance of the USV with the framework is better than that of the USV using the path following method. In addition, it is not necessary to arrange the path of the USV beforehand; the path is autonomously steered to the destination instead. With the benefit of the simple control law, the architecture is available for various levels of hardware.

title: Deep learning based segmentation methods for computer assisted fetal surgery
abstract: This thesis focuses on the development of deep learning based image processing techniques for the detection and segmentation of fetal structures in magnetic resonance imaging (MRI) and 3D ultrasound (US) images of singleton and twin pregnancies. Special attention is laid on monochorionic twins affected by the twinto twin transfusion syndrome (TTTS) which is characterized by the presence of blood vessels located on the surface of the shared placenta. One of the most effective treatments for TTTS is fetoscopic laser photocoagulation, which consists in clogging the blood vessels connecting the twins. Due to the complexity of this intervention, a conscious preoperative planning becomes essential. In particular, selecting the best entry point for the fetoscope, in order to be able to reach all the anastosomes, is key for the intervention. We propose the first TTTS fetal surgery planning and simulation platform, which incorporates novel algorithms into a flexible C+ and MITK based application. The software allows full exploration of the intrauterine environment by simulating the fetoscope camera as well as the laser ablation, helping to determine the best entry point for the fetoscope, and training surgeons' movements and trajectories ahead of operation. Different approaches are utilized to automatically segment the mother's soft tissue, uterus, placenta, its peripheral blood vessels, and umbilical cord from multiple (axial, sagittal and coronal) MRI views or a super resolution reconstruction. In particular, 3D Gabor filters, texture features and Support Vector Machines are employed for placenta segmentation, and curvature based corner detector for peripheral vessels detection. The mother's womb is localized and subsequently segmented using reinforcement learning and capsule networks. Finally, the cord is tackled through 3D convolutional networks with long short term memory (LSTM) Complementarily, (conditional) generative adversarial networks (GANs) are used for segmentation of fetal structures from (3D) US. A cascade (stack) of GANs is implemented to simultaneously generate synthetic fetal US images from normal and pathological cases, perform multi class segmentation of the placenta, ecographic shadows, and the peripheral blood vessels, and finally reconstruct (compensate) placenta acoustic shadows. The umbilical cord insertion is also localized from color Doppler US. Finally, we present a comparative study of deep learning approaches and Radiomics over the segmentation performance of several fetal and maternal anatomies in both MRI and 3D US. With regard to Radiomics, ninety four radiomics features are utilized, the optimal ones for each anatomy are identified and fed into a Support Vector Machine with instance balancing. The work in this thesis has been validated on a large database of clinical cases, with successful results. Although experiments are oriented towards TTTS, results indicate that these methodologies could be extended to other fetal disorders.

title: Intelligent Image Recognition System for Marine Fouling Using Softmax Transfer Learning and Deep Convolutional Neural Networks
abstract: The control of biofouling on marine vessels is challenging and costly. Early detection before hull performance is significantly affected is desirable, especially if "grooming" is an option. Here, a system is described to detect marine fouling at an early stage of development. In this study, an image of fouling can be transferred wirelessly via a mobile network for analysis. The proposed system utilizes transfer learning and deep convolutional neural network (CNN) to perform image recognition on the fouling image by classifying the detected fouling species and the density of fouling on the surface. Transfer learning using Google's Inception V3 model with Softmax at last layer was carried out on a fouling database of 10 categories and 1825 images. Experimental results gave acceptable accuracies for fouling detection and recognition.

title: Coating damage localization of naval vessels using artificial neural networks
abstract: For the localization of coating damages of naval vessels numerical simulation using the FEM software COMSOL Multiphysics were carried out to calculate the corresponding underwater electric potential (UEP) signature. Therefore, we defined said damages at random hull surface positions and used the information provided by the impressed current cathodic protection (ICCP) system, more exactly the cathodic current itself, and the calculated UEP signatures as input parameters to train an artificial neural network (ANN) for predicting the coating damage location. With this deep learning approach, more than 90 of all coating damages are predicted correctly, considering a generic ship model with 50 m length, whose hull is divided into 12 different sectors. Even the mere use of ICCP currents as highly aggregated input parameters for the ANN lead to a satisfactory prediction rate over 80 within the predefined sectors, thus providing quite accurate results using minimal amount of data.