A Deep Learning-Based Framework for End-To-End Identification of IMO Numbers on Ships

Ma, Haowei; Zhang, Di; Fan, Liang; Li, Yuli; Xu, Zidong

doi:10.1109/ictis60134.2023.10243842

Cited by 2 publications

References 19 publications

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Ship detection and identification for maritime security and safety based on IMO numbers using deep learning

Khan,

Liu,

Prabhu

et al. 2024

Artificial Intelligence for Security and Defence Applications II

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In marine safety and security, the ability to rapidly, autonomously, and accurately detect and identify ships is the highest priority. This study presents a novel approach using deep learning to accurately identify ships based on their International Maritime Organisation (IMO) numbers. The performance of various sophisticated deep learning models, such as YOLOv8, RetinaNet, Faster R-CNN, EfficientDet, and DETR, was assessed in accurately identifying IMO numbers from images. The RetinaNet and Faster R-CNN models achieved the highest mAP50-95 scores of 70.0% and 64.1%, respectively, with inference times of low scale. On the other hand, YOLOv8, with a slightly better mAP50-95 of 65.1%, showed an exceptional balance between accuracy and speed (9.20 ms), making it well-suited for real-time applications. However, models like EfficientDet and DETR experienced difficulties achieving lower mAP50-95 values of 33.65% and 48.7%, respectively, especially when analysing low-resolution images. Following detection, the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) was used to improve the clarity of extracted IMO digits. It is followed by applying Easy Optical Character Recognition (EasyOCR) for accurate extraction. Despite the enhancements, minor identification errors continued, suggesting a requirement for additional refinement. These findings reveal the capacity of deep learning to significantly augment maritime security by enhancing the efficiency and precision of ship identification.

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Ship detection and identification for maritime security and safety based on IMO numbers using deep learning

Khan,

Liu,

Prabhu

et al. 2024

Artificial Intelligence for Security and Defence Applications II

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Enhanced YOLOv8-Based System for Automatic Number Plate Recognition

Al-Hasan,

Bonnefille,

Bensaali

2024

Technologies

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This paper presents an advanced automatic number plate recognition (ANPR) system designed specifically for Qatar’s diverse license plate landscape and challenging environmental conditions. Leveraging the YOLOv8 deep learning model, particularly the YOLOv8s variant, we achieve state-of-the-art accuracy in both license plate detection and number recognition. Our innovative approach includes a comprehensive dataset enhancement technique that simulates adverse conditions, significantly improving the model’s robustness in real-world scenarios. We integrate edge computing using a Raspberry Pi with server-side processing, demonstrating an efficient solution for real-time ANPR applications. The system maintains greater than 93% overall performance across various environmental conditions, including night-time and rainy scenarios. We also explore the impact of various pre-processing techniques, including edge detection, k-mean thresholding, DBSCAN, and Gaussian mixture models, on the ANPR system’s performance. Our findings indicate that modern deep learning models like YOLOv8 are sufficiently robust to handle raw input images and do not significantly benefit from additional pre-processing. With its high accuracy and real-time processing capability, the proposed system represents a significant advancement in ANPR technology and is particularly suited for Qatar’s unique traffic management needs and smart city initiatives.

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A Deep Learning-Based Framework for End-To-End Identification of IMO Numbers on Ships

Cited by 2 publications

References 19 publications

Ship detection and identification for maritime security and safety based on IMO numbers using deep learning

Ship detection and identification for maritime security and safety based on IMO numbers using deep learning

Enhanced YOLOv8-Based System for Automatic Number Plate Recognition

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