The interest in video surveillance has been increasing in the fields of maritime industry in the past decade. Maritime transportation system is a vital part of the world’s economy and the extent of global ship traffic is increasing. This trend encourages the development of intelligent surveillance systems in the maritime zone. The development of intelligent surveillance systems includes sensor and data fusion, which incorporates multispectral and multisensory data to replace the traditional approach with radars only. Video cameras are widely used since they capture images of greater resolution than most sensor systems. Also, combined with video analytics they provide sensors with high capability, complex pattern recognition analytics, and multiple variables for the decision making process. In this paper, an overview of a small part of the system is presented – horizon detection.

This paper deals with the automatic traffic control of vessels moving through the marine canal traffic system. Dangerous vessel deadlock situations may occur in case of vessels' irregular moving through the system. To avoid this, the vessel traffic is supervised and controlled by traffic lights. Derived supervisor is maximally permissive (responsible for vessels' stopping only in the case of dangerous situation and until this situation elapses). This paper shows a formal method of calculating such supervisor by using Petri net. To ensure deadlock free operation of supervisor, the paper proposes finding and controlling critical minimal siphons (specific set of places in the Petri net which are responsible for deadlock). The supervisor is verified using computer simulation.

Since illumination variations may cause the misinterpretation of data for various higher vision applications and algorithms, this study aims to reduce such influence. In order to obtain a motion mask, which is input for a higher vision application, wavelet coefficients are calculated by applying two-dimensional lifting wavelet transform with two mother wavelets. Energy is calculated from the obtained wavelet coefficients. Morphological operations are used to improve output image. The developed algorithm is a robust algorithm further reducing false alarm readings caused by illumination variations (better false detection rate and percentage of correct classifications).

Nowadays, it is well known that on the ship, free space is at a premium. Unfortunately, every part on the ship has its duration before the failure, and the spare parts occupy the space. The broken part must be replaced when the failure occurs to maintain the ship’s operation. Developing 3D printer technology and particular material technology, it has become possible to print a spare part that can replace broken. However, due to hazardous environments (salt, humidity, vibrations, etc.), printed parts change their properties. Electrical capacity and further dielectric permittivity is a parameter or metric that has to be monitored since it directly influences the printed part material structure. Therefore, this paper aims to research the impact of relative dielectric constant in additive manufacturing on printed ship’s spare parts since infill patterns and density were changed due to a hazardous environment. The experiment, in which the three shaped material samples are created, consists of the following equipment, the Ultimaker S5 3D printer, Polylactic Acid (PLA) and Acrylonitrile–Butadiene–Styrene (ABS) materials, and LC HM 8018 m. Results show relative dielectric constant changes between 1.7778 and 2.8141 for PLA and between 2.1979 and 2.9989 for ABS, depending on infill density and pattern. ANOVA test for ABS is performed to investigate how the calculated dielectric constant relates to the infill density for various infill shapes. Scores are: F = 154.3773, F crit = 5.1432, and p = 6.9269·10 –6 . ANOVA test for PLA resulted in scores F = 18.911, F crit = 5.1432, and p = 0.0022.

Future trends in maritime technology include the application of additive technology in spare parts management. Nowadays, 3D printing has become an integral technology in many fields. Maritime industry is one of the fields where 3D printing has become a focus of research. To prepare Electro-technical Officers (ETOs) for the future, it is necessary to investigate parameters which help with deciding whether to use additive technology or to order a spare part. This paper aims to research parameters influencing spare parts printing as a job carried out by ETOs aboard ships. Conclusions about the filament density and quality of the printed parts are derived and presented. Suggestions for future work and possible applications are given.

Signal processing plays a pivotal role in information gathering and decision making. This paper presents and compares different signal processing techniques used in marine and navy applications, primarily based on using wavelets as kernel. The article covers Fourier transform, time frequency wavelet based techniques such as bandelets, contourlets, curvelets, edgelets, wedgelets, shapelets, and ridgelets. In the example section of the paper, several transform techniques are presented and commented on the harbour surveillance video stream example.

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