Computer Vision-Based Early Detection of Container Loss at Sea

2026-04-27

AI Summary

This study addresses the critical risk of container stack collapse and loss overboard during vessel motion under harsh sea conditions, which poses significant safety, environmental, and economic threats. To mitigate this challenge, the authors propose a low-cost computer vision system leveraging existing onboard cameras—requiring no additional sensors—that enables container-level micro-motion detection and quantification of relative displacement for the first time. By integrating object segmentation, optical flow-based temporal tracking, and residual motion analysis, the method effectively isolates and monitors inter-tier movements within container stacks in real-world shipborne video footage. The approach significantly enhances cargo safety, operational resilience, and compliance with International Maritime Organization (IMO) regulations, providing a foundation for early-warning systems and timely intervention.

Technology Category

• Computer Vision: Motion & Tracking
• Intelligent Robots: Motion and Path Planning
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Security and Privacy: Large-scale security measurements

Abstract

Containerised shipping underpins global trade, yet container loss at sea remains a persistent safety, environmental, and economic challenge. Despite compliance with Cargo Securing Manuals, dynamic maritime conditions such as vessel motion, wind loading, and severe sea states can progressively destabilise container stacks, leading to overboard losses. With the new International Maritime Organisation's (IMO) mandatory reporting requirements for lost containers, there is an urgent need for a reliable, evidence-based early detection solution for destabilised containers. This study showcases a low-cost, retrofittable computer vision-based system for early detection of destabilised containers using existing onboard cameras. The framework integrates object segmentation to isolate container stacks, temporal object tracking using optical flow and individual objects' residual motion extraction to quantify relative movement. Experimental evaluation on real onboard ship footage demonstrates that the proposed pipeline effectively isolates container-level motion under challenging conditions of varying sea states and visibility conditions. By enabling early alerts for crew intervention and navigational adjustment, the proposed approach enhances cargo safety, operational resilience, and regulatory compliance.

Problem

• container loss
• early detection
• maritime safety
• container destabilisation
• computer vision

Innovation

• computer vision
• container loss detection
• optical flow
• object segmentation
• maritime safety