Efficient container handling and early damage detection are critical for minimizing operational delays, reducing costs, and ensuring safety in global maritime logistics.This work presents a deep learning-based methodology for real-time container tracking and automated damage detection during crane unloading operations at container terminals.We develop and deploy two specialized YOLOv12-based object detection models: one for identifying containers in motion and another for detecting structural damages such as bents, dents, and holes.Our models are trained and evaluated on a real-world dataset curated from video feeds captured at the EUROGATE Container Terminal in Limassol, Cyprus.The system is designed for robust performance under realistic terminal conditions, including variable lighting and motion.Our models achieve high detection accuracy, with a mAP50 of 0.99 for container detection and 0.75 for damage detection, substantially outperforming existing benchmarks.These results highlight the practical potential of our method for improving efficiency and safety in automated maritime logistics.

The Internet of things (IoT) ecosystem provides a platform for the connectivity of interrelated smart devices to automate manual processes and reduce labor costs.IoT has brought significant benefits to all industries, including maritime, as various objects (e.g., ports, ships, agents, etc.) are connected to gather and share information within the maritime ecosystem.The innovative technological aspects of IoT are promoting the effective collaboration between the research community and the maritime industry, for enhancing the performance of maritime transportation systems.Therefore, this study discusses recent advances delivered by the IoT and other emerging technologies, like machine learning (ML) and computer vision (CV), for smart maritime transportation systems (SMTSs).In particular, this paper presents two specific use cases of SMTSs, namely, predictive maintenance and container damage/seal inspection.Moreover, the key benefits of integrating IoT with ML and CV are highlighted for the above-mentioned use cases.Finally, a discussion is presented to highlight key opportunities along with foreseeable future challenges in adopting these new technologies by the maritime industry.

New requirements, posed by the Next Generation IoT, demand design of novel reference architectures, providing foundation for implementation of Internet of Things (IoT) ecosystems. Building on cloud-native concepts (e.g. microservices, virtualisation, and containerization), a flexible architecture that answers requirements present in recent IoT deployments is introduced. A general description of components of the architecture (grouped in horizontal planes and vertical capabilities) is provided, together with formal definition of architectural views. Moreover, ground is laid for upcoming validation in real-world-anchored scenarios. Functional, node, deployment and data views are presented, each of them addressing concerns of different stakeholder groups, typically involved in an IoT deployments.