This study presents a new real-time computer vision architecture designed for maritime environments that combines YOLO for object detection and PaddleOCR for text recognition. The YOLO algorithm was tuned to identify ships and text regions using a dataset of over 600 annotated photos. Two output layers with good detection accuracy (mAP 0.90, F1-score 0.89) were obtained by removing the smallest detection scale (P3) in order to speed up inference and lower computational complexity. making it appropriate for marine applications with bandwidth constraints. To enhance OCR ‘robustness in low-quality or variable lighting conditions, detected text regions undergo a lightweight preprocessing pipeline consisting of grayscale conversion, contrast enhancement, and noise reduction. The proposed framework enables automated and continuous ship monitoring, thereby supporting compliance verification, port logistics, and security operations in real seaport environments. Furthermore, the architecture demonstrates scalability toward large scale, real-time maritime surveillance systems.
Yahaya et al. (Tue,) studied this question.