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

Authors: Weishan Chen; Peng Gao; Zhigang Zhou

Addresses: College of Architectural and Engineering, Guangzhou Panyu Polytechnic, Guang'zhou, 511487, China ' College of Architectural and Engineering, Guangzhou Panyu Polytechnic, Guang'zhou, 511487, China ' Department of Research and Development, Dragon Spring Technology Co., Ltd, Guang'zhou, 510399, China

Abstract: 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.

Keywords: multi feature fusion; support vector machine; improve SVM; underground water supply pipelines; intelligent defect detection.

DOI: 10.1504/IJISTA.2025.145628

International Journal of Intelligent Systems Technologies and Applications, 2025 Vol.23 No.1/2, pp.169 - 183

Received: 05 Sep 2024
Accepted: 07 Nov 2024
Published online: 09 Apr 2025 *

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