This paper presents a comprehensive study on utilizing artificial intelligence (AI) and advanced detection techniques for the study and monitoring of ships. The primary objective is to prevent various issues, such as ship intrusion detection, ship detection in satellite images, and ship detection in river images. To achieve this, the study proposes innovative methods; including enhancing the capabilities of YOLOv3 and YOLOv8 neural networks to improve the accuracy of ship detection. Additionally, the study leverages IoT technology for real-time tracking and integrates feature fusion modules for more effective information integration. A crucial aspect highlighted in this study is the necessity of controlling pollution caused by ships. By addressing this environmental concern, the study aims to contribute to the preservation of marine ecosystems and enhance maritime safety. The results of the study demonstrate a significant enhancement in detection accuracy, showcasing the potential of these advanced methods for efficient and reliable ship monitoring systems.

The maritime industry, essential for international logistics and trade, increasingly requires precise and reliable control systems for surface vessels, especially during critical operations such as docking and maneuvering in tight areas. This research presents a data-driven framework for predicting ship control commands, specifically focusing on the number of turns and rudder angle, by using hybrid models that combine deep learning and ensemble machine learning techniques. CNNs are used for feature extraction, while recurrent architectures like GRU and LSTM are used to handle the time-series data generated by a three-DOF vessel model operating in a simulated port environment. To improve prediction performance, these deep learning models are combined with Random Forest and Extra Trees regressors. Comparative evaluations show that the GRU+ExtraTree model delivers superior accuracy and responsiveness, particularly during sudden changes in control signals, due to its ability to capture temporal dependencies. The proposed method shows promising potential for real-time trajectory tracking and autonomous docking applications in maritime navigation.

This paper presents a secure blockchain-enabled Automatic Number Plate Recognition (ANPR) framework for Intelligent Transportation Systems (ITS), combining lightweight YOLO-based license plate detection, OCR-based character recognition, and Ethereum smart contracts for tamper-resistant vehicle data management. The study investigates the inference performance of recent object detection architectures, namely YOLOv11, YOLOv12, and YOLO26, on Raspberry Pi 5 and NVIDIA Jetson Nano edge devices. Furthermore, optimizations for ONNX and TensorRT are explored to enhance inference efficiency on embedded hardware. Experimental results demonstrate complementary strengths among the evaluated models. YOLOv11n achieved the best overall detection performance, obtaining the highest recall (0.945), F1-score (0.9645), and mAP@0.5:0.95 (0.692), while YOLOv11n and YOLOv12n achieved identical precision (0.985) and mAP@0.5 (0.963). YOLO26 demonstrates the best deployment efficiency, reducing inference latency to 33.4 ms on Raspberry Pi (ONNX) and 38.9 ms on Jetson Nano, making it suitable for edge applications. Model optimization significantly accelerates inference, reducing YOLOv11 latency on Raspberry Pi from 98.2 ms to 38.3 ms after ONNX conversion. The results demonstrate that the proposed framework effectively balances detection accuracy, computational efficiency, and data security, making it a promising solution for smart parking and next-generation ITS applications.