Morphological segmentation based on edge detection for sewer pipe defects on CCTV images

Abstract

The essential work of sewer rehabilitation is a sewer inspection through diagnoses of sewer pipe defects. At present, image processing and artificial intelligence techniques have been used to develop diagnostic systems to assist engineers in interpreting sewer pipe defects on CCTV images to overcome human’s fatigue and subjectivity, and time-consumption. Based on the segmented morphologies on images, the diagnostic systems were proposed to diagnose sewer pipe defects. However, the environmental influence and image noise hamper the efficiency of automatic diagnosis. For example, the central area of a CCTV image, where is always darker than the surrounding due to the vanishing light and slight reflectance, causes a difficulty to segment correct morphologies. In this paper, a novel approach of morphological segmentation based on edge detection (MSED) is presented and applied to identify the morphology representatives for the sewer pipe defects on CCTV images. Compared with the performances of the opening top-hat operation, which is a popular morphological segmentation approach, MSED can generate better segmentation results. As long as the proper morphologies of sewer pipe defects on CCTV images can be segmented, the morphological features, including area, ratio of major axis length to minor axis length, and eccentricity, can be measured to effectively diagnose sewer pipe defects.