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Proceedings of the 21st Conference on Computer Science and Intelligence Systems (FedCSIS)

Annals of Computer Science and Information Systems, Volume 47

Real-time Verification of Container Seal Integrity using Deep Learning

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DOI: http://dx.doi.org/10.15439/2026F0497

Citation: Sotiris Vasileiadis, , , , ,

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Abstract. Container seal integrity verification represents a critical yet underexplored challenge in maritime logistics security. Early theft detection is 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 detection of container seals during crane unloading operations at container terminals. We develop and deploy two specialized YOLOv12-based object detection models: one for identifying container doors in motion and another for detecting in-place security fixtures, such as standard locks, container seals, and customs seals. Our models are trained and evaluated on a real-world dataset curated from video feeds captured at the EUROGATE Container Terminal in Limassol Port, Cyprus. The system is designed for robust performance under realistic terminal conditions, including variable lighting and motion. Our models achieve high detection accuracy across variable weather conditions, with mAP50 scores of 0.95 for container door detection and 0.74 for seal detection, substantially outperforming existing benchmarks. These findings underscore the practical potential of our methodology for enhancing the efficiency and security of automated maritime logistics.

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