Regulatory requirements for sub-sea oil and gas operators mandates the frequent inspection of pipeline assets to ensure that their degradation and damage are maintained at acceptable levels. The inspection process is usually sub-contracted to surveyors who utilize sub-sea Remotely Operated Vehicles (ROVs), launched from a surface vessel and piloted over the pipeline. ROVs capture data from various sensors/instruments which are subsequently reviewed and interpreted by human operators, creating a log of event annotations; a slow, labor-intensive and costly process. The paper presents an automatic image annotation framework that identifies/classifies key events of interest in the video footage viz. exposure, burial, field joints, anodes, and free spans. The reported methodology utilizes transfer learning with a Deep Convolutional Neural Network (ResNet-50), fine-tuned on real-life, representative data from challenging sub-sea environments with low lighting conditions, sand agitation, sea-life and vegetation. The network outputs are configured to perform multi-label image classifications for critical events. The annotation performance varies between 95.1% and 99.7% in terms of accuracy and 90.4% and 99.4% in terms of F1-Score depending on event type. The performance results are on a per-frame basis and corroborate the potential of the algorithm to be the foundation for an intelligent decision support framework that automates the annotation process. The solution can execute annotations in real-time and is significantly more cost-effective than human-only approaches.

中文翻译：

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使用深度学习自动注释海底管道。


海底石油和天然气运营商的监管要求要求经常检查管道资产，以确保其退化和损坏保持在可接受的水平。检查过程通常分包给使用海底遥控潜水器 (ROV) 的测量员，这些潜水器从水面船舶发射并在管道上进行引导。 ROV 从各种传感器/仪器捕获数据，随后由操作员审查和解释，创建事件注释日志；这是一个缓慢、劳动密集且成本高昂的过程。本文提出了一种自动图像注释框架，可以识别/分类视频片段中感兴趣的关键事件。暴露、掩埋、现场接缝、阳极和自由跨度。所报告的方法利用深度卷积神经网络 (ResNet-50) 的迁移学习，对现实生活中具有低光照条件、沙子搅动、海洋生物和植被的挑战性海底环境的代表性数据进行微调。网络输出配置为对关键事件执行多标签图像分类。根据事件类型，注释性能在准确度方面介于 95.1% 和 99.7% 之间，在 F1-Score 方面介于 90.4% 和 99.4% 之间。性能结果以每帧为基础，证实了该算法作为自动注释过程的智能决策支持框架的基础的潜力。该解决方案可以实时执行注释，并且比纯人工方法更具成本效益。