Research on Ship Classification using Faster Region Convolutional Neural Network for Port Security

Huvudsyftet med studien var att se i vilken grad det går att finna samarbeten genom material- och/eller energiutbyten mellan närliggande anläggningar inom skogsindustrin i Sverige. Genom att göra en inventering av vilka anläggningar som finns inom skogsindustrin och sedan kontakta dessa, sammanställdes en lista över de olika anläggningarna och deras olika samarbeten. Inventeringen gjordes med hjälp av olika branschorganisationer samt sökmotorer på Internet. Utöver detta besöktes också fyra intressanta fall för att ge en inblick i hur dessa samarbeten kan se ut. Studien visar på att den här typen av samarbeten existerar inom skogsindustrin och att drygt en tredjedel av de studerade anläggningarna har någon form av samarbeten rörande dessa frågor. Detta pekar på att man inom skogsindustrin är långt framme när det gäller resursutnyttjande och att möjligheten att minimera sin energi- och materialanvändning hela tiden är en relevant fråga. Det finns med stor sannolikhet ännu fler sådana samarbeten som inte framkommit vid undersökningen och en intressant aspekt är att vid de besök som gjordes upptäcktes samarbeten som inte uppmärksammats vid tidigare kontakter. Av de 152 tillfrågade anläggningarna i inventeringen erhölls svar från 117 stycken vilket tyder på att det finns ett stort intresse för dessa frågor inom skogsindustrin. Flera av de anläggningar som inte hade några samarbeten kring dessa frågor svarade också att de hela tiden undersöker möjligheten till att inleda sådana. Många av samarbetena rörande dessa frågor kretsar kring leveranser av el och ånga samt spån och flis men en del andra intressanta samarbeten har också framkommit. Exempelvis används slam från bioreningsdammar till bränsle, jordförbättringsmedel och som täckmaterial vid deponier. Sammanfattningsvis tyder detta på att skogsindustrin ligger långt framme gällande dessa frågor men att det fortfarande finns mer att göra om energi- och materialanvändningen och därigenom den negativa miljöpåverkan ska minimeras.The aim and objective with this study was to investigate to what extent co-operation through material and energy exchange between adjacent industries among the forest industry in Sweden could be found. First, an inventory of the industries in the forest industry was conducted. Secondly, each company was contacted with questions concerning this issue. Complementary field studies of four specific cases were conducted in order to give an insight to how these co-operations may function in reality. The result of this study illustrates that co-operations among the industries exist in the forest industry sector as more than a third of the investigated industries has some kind of co-operation regarding material and energy exchange with adjacent industries. A total number of 152 industries were identified during the inventory phase and 117 of those industries participated in the study with their own answers. This high participation rate enhances the impression that these are important questions to the forest industry sector. Numerous of the co-operations mentioned revolve around electricity, steam, and by products from sawmills, like woodchips and sawdust. Nevertheless, a few other interesting co-operations have also been revealed during the study, for example; sludge from some of the pulp mills are used as fuel, soil fertilizer and as covering material at landfills. An interesting point is that co-operations, which not were discovered during the earlier correspondence with the industries, in fact were revealed during the field studies. Therefore, the probability that there are more existing co-operations between adjacent industries than the findings in the study reveals, are high. To sum up, this shows that the forest industry is well in advance regarding co-operation through material and energy exchange between adjacent industries. However, there is still a lot to be done if the negative effect on the environment from the forest industry should be minimised

Reliable Navigational Scene Perception for Autonomous Ships in Maritime Environment

Due to significant advances in robotics and transportation, research on autonomous ships has attracted considerable attention. The most critical task is to make the ships capable of accurately, reliably, and intelligently detecting their surroundings to achieve high levels of autonomy. Three deep learning-based models are constructed in this thesis to perform complex perceptual tasks such as identifying ships, analysing encounter situations, and recognising water surface objects. In this thesis, sensors, including the Automatic Identification System (AIS) and cameras, provide critical information for scene perception. Specifically, the AIS enables mid-range and long-range detection, assisting the decision-making system to take suitable and decisive action. A Convolutional Neural Network-Ship Movement Modes Classification (CNN-SMMC) is used to detect ships or objects. Following that, a Semi- Supervised Convolutional Encoder-Decoder Network (SCEDN) is developed to classify ship encounter situations and make a collision avoidance plan for the moving ships or objects. Additionally, cameras are used to detect short-range objects, a supplementary solution to ships or objects not equipped with an AIS. A Water Obstacle Detection Network based on Image Segmentation (WODIS) is developed to find potential threat targets. A series of quantifiable experiments have demonstrated that these models can provide reliable scene perception for autonomous ships

Deep Convolutional Neural Network based Ship Images Classification

Ships are an integral part of maritime traffic where they play both militaries as well as non-combatant roles. This vast maritime traffic needs to be managed and monitored by identifying and recognising vessels to ensure the maritime safety and security. As an approach to find an automated and efficient solution, a deep learning model exploiting convolutional neural network (CNN) as a basic building block, has been proposed in this paper. CNN has been predominantly used in image recognition due to its automatic high-level features extraction capabilities and exceptional performance. We have used transfer learning approach using pre-trained CNNs based on VGG16 architecture to develop an algorithm that performs the different ship types classification. This paper adopts data augmentation and fine-tuning to further improve and optimize the baseline VGG16 model. The proposed model attains an average classification accuracy of 97.08% compared to the average classification accuracy of 88.54% obtained from the baseline model

A ship movement classification based on Automatic Identification System (AIS) data using Convolutional Neural Network

With a wide use of AIS data in maritime transportation, there is an increasing demand to develop algorithms to efficiently classify a ship’s AIS data into different movements (static, normal navigation and manoeuvring). To achieve this, several studies have been proposed to use labelled features but with the drawback of not being able to effectively extract the details of ship movement information. In addition, a ship movement is in a free space, which is different to a road vehicle’s movement in road grids, making it inconvenient to directly migrate the methods for GPS data classification into AIS data. To deal with these problems, a Convolutional Neural Network-Ship Movement Modes Classification (CNN-SMMC) algorithm is proposed in this paper. The underlying concept of this method is to train a neural network to learn from the labelled AIS data, and the unlabelled AIS data can be effectively classified by using this trained network. More specifically, a Ship Movement Image Generation and Labelling (SMIGL) algorithm is first designed to convert a ship’s AIS trajectories into different movement images to make a full use of the CNN’s classification ability. Then, a CNN-SMMC architecture is built with a series of functional layers (convolutional layer, max-pooling layer, dense layer etc.) for ship movement classification with seven experiments been designed to find the optimal parameters for the CNN-SMMC. Considering the imbalanced features of AIS data, three metrics (average accuracy, score and Area Under Curve (AUC)) are selected to evaluate the performance of the CNN-SMMC. Finally, several benchmark classification algorithms (K-Nearest Neighbours (KNN), Support Vector Machine (SVM) and Decision Tree (DT)) are selected to compare with CNN-SMMC. The results demonstrate that the proposed CNN-SMMC has a better performance in the classification of AIS data