YOLO-Based Pipeline Monitoring in Challenging Visual Environments
Journal:
arXiv
Published Date:
Jun 30, 2025
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.
