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Evaluating an inland waterway cargo vessel’s energy efficiency indices

Kalajdžić Milan, Vasilev Matija, Momčilović Nikola

Polish Maritime Research

2022

nr 2

27--34

Although the International Maritime Organization (IMO) introduced the energy efficiency requirements for ships more than a decade ago, to date, inland navigation has not been affected by corresponding regulations at all. Therefore, inland waterway vessels are left with no mandatory requirements that could push their technology into more energy efficient design. Fortunately, there are certain pioneering attempts to define energy efficiency criteria for inland vessels. This paper tries to gather and provide a review of such methods. Moreover, a typical Danube cargo inland vessel’s data are used to evaluate their current energy efficiency levels with respect to provisional criteria. Consequently, two methods are found and used here. They are both based on IMO’s energy efficiency concept but modified for the inland waterway vessels. The methods delivered a significant difference in applicability and were difficult to compare. Moreover, shallow and deep-water effects are explored in the same regard but provided unsound conclusions. The final results displayed discrepancies in energy efficiency levels for the same vessels and so the methodology should be improved and harmonised, if it is to be introduced as mandatory for inland waterway vessels. The analysis provided a glimpse into the current condition of the traditional design of the Danube inland fleet, with respect to the emerging energy efficiency policies.

Selection of an artificial pre-training neural network for the classification of inland vessels based on their images

Bobkowska Katarzyna, Bodus-Olkowska Izabela

Zeszyty Naukowe Akademii Morskiej w Szczecinie

2021

nr 67 (139)

91--97

Artificial neural networks (ANN) are the most commonly used algorithms for image classification problems. An image classifier takes an image or video as input and classifies it into one of the possible categories that it was trained to identify. They are applied in various areas such as security, defense, healthcare, biology, forensics, communication, etc. There is no need to create one’s own ANN because there are several pre-trained networks already available. The aim of the SHREC projects (automatic ship recognition and identification) is to classify and identify the vessels based on images obtained from closed-circuit television (CCTV) cameras. For this purpose, a dataset of vessel images was collected during 2018, 2019, and 2020 video measurement campaigns. The authors of this article used three pre-trained neural networks, GoogLeNet, AlexNet, and SqeezeNet, to examine the classification possibility and assess its quality. About 8000 vessel images were used, which were categorized into seven categories: barge, special-purpose service ships, motor yachts with a motorboat, passenger ships, sailing yachts, kayaks, and others. A comparison of the results using neural networks to classify floating inland units is presented.