YOLO-Based Pipeline Monitoring in Challenging Visual Environments

TL;DR

This paper investigates the use of advanced YOLO-based AI models to improve subsea pipeline monitoring in low-visibility underwater environments, focusing on detection accuracy and robustness under challenging conditions.

Contribution

It provides a comparative analysis of YOLOv8 and YOLOv11 variants for pipeline detection in complex underwater scenes, highlighting the superior performance of YOLOv11.

Findings

YOLOv11 outperforms YOLOv8 in detection accuracy

Model variants tailored for segmentation improve performance

AI-enhanced monitoring enables more reliable subsea pipeline inspection

Abstract

Condition monitoring subsea pipelines in low-visibility underwater environments poses significant challenges due to turbidity, light distortion, and image degradation. Traditional visual-based inspection systems often fail to provide reliable data for mapping, object recognition, or defect detection in such conditions. This study explores the integration of advanced artificial intelligence (AI) techniques to enhance image quality, detect pipeline structures, and support autonomous fault diagnosis. This study conducts a comparative analysis of two most robust versions of YOLOv8 and Yolov11 and their three variants tailored for image segmentation tasks in complex and low-visibility subsea environments. Using pipeline inspection datasets captured beneath the seabed, it evaluates model performance in accurately delineating target structures under challenging visual conditions. The results indicated that YOLOv11 outperformed YOLOv8 in overall performance.