Automated surveillance systems based on machine learning and computer vision constantly evolve to improve shipping and assist port authorities. The data obtained can be used for port and port property surveillance, traffic density analysis, maritime safety, pollution assessment, etc. However, due to the lack of datasets for video surveillance and ship classification in real maritime zones, there is a need for a reference dataset to compare the obtained results. This paper presents a new dataset for estimating detection and classification performance which provides versatile ship annotations and classifications for passenger ports with a large number of small- to medium-sized ships that were not monitored by the automatic identification system (AIS) and/or the vessel traffic system (VTS). The dataset is considered general for the Mediterranean region since many ports have a similar maritime traffic configuration as the Port of Split, Croatia. The dataset consists of 19,337 high-resolution images with 27,849 manually labeled ship instances classified into 12 categories. The vast majority of the images contain the port and starboard sides of the ships. In addition, the images were acquired in a real maritime zone at different times of the year, day, weather conditions, and sea state conditions.

Autonomous ships are in experimental stage nowadays with Maritime Autonomous Surface Ships (MASS) already defined by IMO. Since MASS rely heavily on communications, security of communication systems and data security is critical. Secure communication is required to avoid bad actors to interfere with the communications or seizing control of a autonomous ship. In this paper, implementation of blockchain technology to improve autonomous vessels control security is investigated. This technology is already used in maritime bill of lading, acts on ship's technical inspection and for more accurate container tracking etc. The paper is organized as follows: first section describes current status on autonomous ships, basic definitions and terms, second section describes what is blockchain technology and how does it work, third section deals with blockchain technology applications with the proposed usage of the technology in autonomous vessels control scheme.

Monitoring and counting maritime traffic is important for efficient port operations and comprehensive maritime research. However, conventional systems such as the Automatic Identification System (AIS) and Vessel Traffic Services (VTS) often do not provide comprehensive data, especially for the diverse maritime traffic in Mediterranean ports. The paper proposes a real-time vessel counting system using land-based cameras is proposed for maritime traffic monitoring in ports, such as the Port of Split, Croatia. The system consists of a YOLOv4 Convolutional Neural Network (NN), trained and validated on the new SPSCD dataset, that classifies the vessels into 12 categories. Further, the Kalman tracker with Hungarian Assignment (HA) algorithm is used as a multi-target tracker. A stability assessment is proposed to complement the tracking algorithm to reduce false positives by unwanted objects (non-vessels). The evaluation results show that the system has an average counting accuracy of 97.76% and an average processing speed of 31.78 frames per second, highlighting its speed, robustness, and effectiveness. In addition, the proposed system captured 386% more maritime traffic data than conventional AIS systems, highlighting its immense potential for supporting comprehensive maritime research.

Maritime traffic monitoring systems are particularly important in Mediterranean ports, as they provide more comprehensive data collection compared to traditional systems such as the Automatic Identification System (AIS), which is not mandatory for all vessels. This paper improves the existing real-time maritime traffic monitoring systems by introducing a distance estimation algorithm for monocular cameras, which aims to provide high quality maritime traffic metadata collection for traffic density analysis. Two distance estimation methods based on a pinhole camera model are presented: the Vessel-Focused Distance Estimation (VFDE) and the novel Vessel Object-Focused Distance Estimation (VOFDE). While VFDE uses the predefined height of a vessel for distance estimation, VOFDE uses standardized dimensions of objects on the vessel, detected with a Convolutional Neural Network (CNN) for instance segmentation to enhance estimation accuracy. Our evaluation covers distances up to 414 m, which is significantly beyond the scope of previous studies. When compared to the distances measured with a precise instrument, VOFDE achieves a Percentage Deviation Index (PDI) of 1.34% to 9.45%. This advance holds significant potential for improving maritime surveillance with monocular cameras and is also applicable in other areas, such as low-cost maritime vehicles equipped with single cameras.

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.