Event Classification on Subsea Pipeline Inspection Data Using an Ensemble of Deep Learning Classifiers - Dang, Truong; Nguyen, Tien Thanh; Liew, Alan Wee-Chung; Elyan, Eyad (Cognitive computation, 2025)
Subsea pipelines are the backbone of the modern oil and gas industry, transporting a total of 28% of global oil production. Due to several factors, such as corrosion or deformations, the pipelines might degrade over time, which might lead to serious economic and environmental damages if not addressed promptly. Therefore, it is crucial to detect any serious damage to subsea pipelines before they cause dangerous catastrophes. Inspections of subsea pipelines are usually made using a Remote Operating Vehicle and the inspection data is usually processed manually, which is subject to human errors, and requires experienced Remote Operating Vehicle operators. It is thus necessary to automate the inspection process to enable more efficiency as well as reduce costs. Besides, it is recognised that specific challenges of noisy and low-quality inspection data arising from the underwater environment prevent the industry from taking full advantage of the recent development in the Artificial Intelligence field to the problem of subsea pipeline inspection. In this paper, we developed an ensemble of deep learning classifiers to further improve the performance of single deep learning models in classifying anomalous events on the subsea pipeline inspection data. The output of the proposed ensemble was combined based on a weighted combining method. The weights of base classifiers were found by minimising the difference between the weighted combining result and the given associated ground truth annotation information. Three inspection datasets, gathered from different oil and gas companies in the United Kingdom, were analysed. These datasets were recorded under varying conditions and include a range of anomalies. The results showed that the proposed ensemble achieves around 78% accuracy on two datasets and more than 99% accuracy on one dataset, which is better compared to base classifiers and two popular ensembles.