Real-Time Pipeline Fault Detection in Water Distribution Networks Using You Only Look Once v8.

Area of Science:

• Water resource management
• Artificial intelligence in infrastructure monitoring
• Computer vision for defect detection

Background:

• Traditional pipeline inspection methods are often inefficient, costly, and error-prone.
• Ensuring a reliable supply of clean water necessitates effective detection of pipeline faults.
• There is a need for advanced, automated solutions for real-time pipeline monitoring.

Purpose of the Study:

• To develop and evaluate an AI-based model for detecting pipeline defects using image analysis.
• To compare the performance of the AI model against traditional inspection methods.
• To assess the model's robustness in diverse environmental conditions for water management systems.

Main Methods:

• Utilized the YOLOv8 object detection model for identifying pipeline faults such as leaks, cracks, and corrosion.
• Trained the YOLOv8 model on a comprehensive dataset of labeled pipeline images.
• Conducted experiments on a real-world dataset to validate the model's detection accuracy and efficiency.

Main Results:

• The AI-based model demonstrated significantly higher detection accuracy compared to traditional methods.
• Experiment 3 achieved a superior overall mean Average Precision (mAP50) of 76.1% in fault detection.
• The model showed robustness to variations in lighting, camera angles, and occlusions.

Conclusions:

• The AI-based YOLOv8 model offers a promising and effective approach for automated pipeline fault detection in water management.
• Implementing this AI solution can enhance operational efficiency, reduce costs, and prevent water loss and contamination.
• The study highlights the potential of AI-driven image analysis for improving the reliability and sustainability of water infrastructure.