Computer Vision-Based Early Detection of Container Loss at Sea

TL;DR

This paper presents a cost-effective computer vision system that uses onboard cameras to detect destabilized containers early, improving safety and compliance in maritime shipping.

Contribution

A novel, retrofittable vision-based framework combining object segmentation and motion tracking for early container loss detection at sea.

Findings

Effective in isolating container motion under challenging sea conditions

Demonstrates reliable early detection capabilities from onboard footage

Enhances safety and regulatory compliance in maritime operations

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.