mlsanityData to 14 Oct 2026

YOLO-Based Pipeline Monitoring in Challenging Visual Environments

arXiv 2507.02967 ↗First posted 30 Jun 2025cs.CV cs.AIcited by 0

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Abstract (arXiv)

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.

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References · 17

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5 more
  1. 2024YOLOv11: An Overview of the Key Architectural Enhancementscited by 425
  2. 2017LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentationcited by 214
  3. 2024SubPipe: A Submarine Pipeline Inspection Dataset for Segmentation and Visual-inertial Localizationcited by 7
  4. 2021Two layer Ensemble of Deep Learning Models for Medical Image Segmentationcited by 2
  5. 2024Deep learning-based fault identification in condition monitoringcited by 1

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