A comparison of the performance of 2D and 3D convolutional neural networks for subsea survey video classification

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Abstract

Utilising deep learning image classification to auto-matically annotate subsea pipeline video surveys can facilitate the tedious and labour-intensive process, resulting in significant time and cost savings. However, the classification of events on subsea survey videos (frame sequences) by models trained on individual frames have been proven to vary, leading to inaccuracies. The paper extends previous work on the automatic annotation of individual subsea survey frames by comparing the performance of 2D and 3D Convolutional Neural Networks (CNNs) in classifying frame sequences. The study explores the classification of burial, exposure, free span, field joint, and anode events. Sampling and regularization techniques are designed to address the challenges of an underwater inspection video dataset owing to the environment. Results show that a 2D CNN with rolling average can outperform a 3D CNN, achieving an Exact Match Ratio of 85% and F1-Score of 90%, whilst being more computationally efficient.
Original languageEnglish
Number of pages10
Publication statusAccepted/In press - 31 Jul 2021
EventOceans 2021 - San Diego, San Diego, United States
Duration: 20 Sep 202123 Sep 2021
https://global21.oceansconference.org/

Conference

ConferenceOceans 2021
Country/TerritoryUnited States
CitySan Diego
Period20/09/2123/09/21
Internet address

Keywords

  • image classification
  • deep learning
  • subsea inspection
  • video classification
  • underwater pipelines
  • video surveys
  • convolutional neural networks (CNN)

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