arXiv:2604.24193cs.CV2026-04中稿 · and Presented at S…

用船载摄像头识别海上集装箱失稳,提前预警避免丢失

Computer Vision-Based Early Detection of Container Loss at Sea

论文配图:Computer Vision-Based Early Detection of Container Loss at Sea
图 1 · 摘自论文原文
  • 通过目标分割和光流追踪,量化集装箱相对运动
  • 在不同海况和能见度下仍能有效检测集装箱位移
  • 低成本可改造系统,适合航运安全与合规监管

集装箱运输支撑全球贸易,但海上集装箱丢失仍是安全、环境与经济的重大挑战。尽管遵循货物固定手册,船舶运动、风载荷和恶劣海况仍可能逐步导致集装箱堆叠失稳,引发坠海。随着国际海事组织(IMO)对丢失集装箱实施强制报告要求,亟需可靠、基于证据的早期检测方案。本研究展示了一种低成本、可改装的计算机视觉系统,利用现有船载摄像头实现集装箱失稳的早期检测。该框架结合目标分割分离集装箱堆,通过光流与个体残差运动提取实现时序跟踪,量化相对运动。在真实船载视频上的实验表明,该方法在不同海况与能见度条件下均能有效分离集装箱级运动。该系统可为船员干预与航行调整提供早期预警,提升货物安全、运营韧性与监管合规性。

原文摘要 · Abstract (English)

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.

视觉检测航运安全集装箱

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