YOLO-Based Pipeline Monitoring in Challenging Visual Environments
This addresses condition monitoring for subsea pipeline inspection, but it is incremental as it compares existing YOLO variants on a specific dataset.
This study tackled the problem of monitoring subsea pipelines in low-visibility underwater environments by comparing YOLOv8 and YOLOv11 variants for image segmentation, finding that YOLOv11 outperformed YOLOv8 in overall performance.
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.