Abstract
The object of the research is in-pipe defect detection and classification. The primary problem to be solved is the inefficiency, high cost, and inaccuracy of traditional manual inspection methods, which are often time-consuming and prone to human error. The results obtained include the creation of a multi-modal platform that integrates Red-Green-Blue (RGB) imaging and depth data with advanced artificial intelligence algorithms, Canny edge detection, and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering, achieving a 93 % mean Average Precision (mAP) in detecting and classifying various defects such as cracks, corrosion, and debris. A brief interpretation of the findings reveals that the high performance is due to the synergy between multi-modal sensing, artificial intelligence pattern recognition, and robust robotic navigation. This integrated approach ensures that the system not only detects defects accurately but does so in real time. Features and characteristics of the obtained results that directly address the identified problem include real-time high-precision defect identification, and reduced inspection downtime. As a result, inspection time is shortened, costs are lowered, and the safety of the pipeline system is increased, leading to accurate measurement of indicators (93 % mAP) and a reduction in occupational safety risks. The developed system is designed for use in traditional industrial environments, especially in large pipeline networks and in conditions where traditional methods are ineffective