An intelligent detection of defects in underground water supply pipelines based on multi feature fusion and improved SVM

To enhance the precision and speed of detecting flaws in water distribution conduits, a sophisticated detection approach leveraging multi-feature integration and an enhanced SVM for subterranean water supply pipeline imperfections is introduced. Initially, the KT-965CCTV pipeline inspection robot is employed to capture endoscopic visuals of underground water conduits. Subsequently, SIFT scale-invariant characteristics, GLCM texture descriptors, and Hu's invariant moment geometric attributes are extracted from the pipeline imagery. The K-means algorithm constructs a visual lexicon, and through sequential integration, these features are amalgamated. The SVM framework is then refined using a binary tree structure, facilitating the establishment of an optimal classification boundary and decision-making function. The fused feature outcomes are fed into the refined SVM to enable automated detection of pipeline anomalies. Experimental data indicate that our methodology maintains a defect detection accuracy exceeding 94% while decreasing the duration of the detection process.