Detection and Isolation of Interior Defects Based on Image Processing and Neural Networks: HDPE Pipeline Case Study

Abstract

This paper investigates the condition of polyethylene (PE) pipelines as a case study. This study introduces a novel method to detect and diagnose defects of high-density polyethylene (HDPE) pipes. The pipe defect detector technique (PDDT) is designed to capture and process the images from the inner surface of pipes. Consequently, PDDT is one of the nondestructive ways to investigate possible defects in pipes. The PDDT’s outcome offers valuable information regarding the shape, orientation, and length of defects in the inner surface of the pipe. This information plays an important role in defining the lifetime of the pipe and fault prediction. In this paper, a database consisting of a total 35 images was used to train, test, and verify a neural network system. For this purpose, input image quality was enhanced by applying Gabor and entropy filters. Then, the trained neural network was used to classify the input images into five defect categories. These categories are defined in a way to describe the shape and the orientation of the defects. Afterward, a curve completion method (CMM) that effectively derives the defect dimensions such as diameter and length was introduced. Finally, the life prediction methods that can use PDDT’s result to predict the time that actual fault may occur in the pipe are discussed.