Research on Intelligent Monitoring and Maintenance Technology of Municipal Pipelines Based on Artificial Intelligence

Municipal pipelines include a range of vital infrastructure, such as water pipes that provide communities with clean water and sewage pipes that manage wastewater. For effective data transport and communication, pipeline systems frequently use optical cables and telecommunication wires. These pipes together serve as the foundation of metropolitan utility networks. Intelligent monitoring is essential to maintaining their condition and guaranteeing dependable service. The objective is to develop an Artificial Intelligence (AI)-based model for intelligent monitoring and maintenance technology for municipal pipelines. Sewer pipelines defect images are utilized for training and evaluation of the research. This research proposed an AI-driven Enhanced Generative Adversarial Network (EGAN) to enhance the monitoring and maintenance processes for municipal sewer pipelines by detecting and classifying defects from image data. To collect image data of defects from sewer pipelines accurately, the data were preprocessed using a Wiener filter to remove noise from acquired data. ResNet18 is used to extract spatial features, such as edges, textures, and shapes directly from images. Flamingo Search Optimization (FSO) was used to optimize feature selection based on the defined objective function. Findings demonstrate the proposed approach was validated on inspection images from pipelines, achieving an improvement in high-level defect detection with an accuracy of 98.44%, precision of 95.76%, recall of 96.85%, and an F1-score of 97.60%. While distinguishing between certain defect types remains challenging, the AI-based system demonstrated notable advancements over traditional methods, offering a robust solution for intelligent pipeline monitoring and maintenance.