Deep learning-based method for foundation environmental monitoring of offshore platform

doi:10.13474/j.cnki.11-2246.2025.0603

Abstract

Abstract: Structural health monitoring (SHM) of offshore platform and ancillary pipelines pose challenges, including low efficiency of manual labor, high time costs, and reliance on subjectivity of technicians with existing knowledge. To address these issues, an intelligent, deep learning (DL)-based algorithm for monitoring the foundation subsea environment of offshore platform was proposed. This approach can prevent manual subjective factors from affecting image interpretation and enable early detection of foundation environmental disasters using an all-weather automated real-time offshore platform. Firstly, experiments were conducted to determine the most appropriate basic network structure for real-time classification of subsea 3D sonar images using computer vision(CV) algorithms based on DL that are presently in use. Secondly, the channel-priority convolutional attention (CPCA) module was employed for the improvement of the experimentally selected VGG-11 algorithm, and the effectiveness of the CPCA-VGG algorithm was verified by the Grad-CAM algorithm. The experimental results demonstrate that the CPCA-VGG algorithm assessment criteria achieve: Acc_Top-1 97.35%,Acc_Top-5 100.00%, mean precision and mean recall is 98.62% and 98.44%, when it was applied in offshore platform foundation environment classification. This algorithm can better meet the practical engineering needs of real-time monitoring of various environments based on offshore platforms and preliminary early warning of disasters.

Key words: structural health monitoring, offshore platform, subsea environment monitoring, computer vision, deep learning, 3D real-time sonar image

CLC Number:

P229

ZHANG Chao, XIONG Chunbao, LIAN Sida. Deep learning-based method for foundation environmental monitoring of offshore platform[J]. Bulletin of Surveying and Mapping, 2025, 0(6): 12-17.

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