Automated Defect Detection Tool For Sewer Pipelines

Abstract

In sewer networks, the economic effects and costs that result from a pipeline break are rising sharply. In Qatar, majority of the sewer network pipelines were installed in the last 20 years and are currently in poor condition and constantly deteriorating. As a result, there is huge demand for inspection and rehabilitation of sewer pipelines. In addition to being inaccurate, current Practices of sewer pipelines inspection are time consuming and may not keep up with the deterioration rate of the pipelines. Consequently, this research aims to develop an automated tool to detect different defects such as cracks, deformation, settled deposits and joint displacement in sewer pipelines. The automated approach is dependent upon using image-processing techniques and several mathematical formulas to analyze output data from CCTV camera photos. Given that one inspection session can result in hundreds of CCTV Camera footage, introducing an automated tool would help yield faster results. Additionally, given the subjective nature of most defects, it will result in more systematic results since the current method rely heavily on the operator's experience. The automated tool was able to successfully detect cracks, displaced joints, ovality and settled deposits in pipelines using CCTV Camera inspection output footage. Using two different data sets, the constructed Matlab code could successfully differentiate between cracks and displaced joints with an overall crack detection success rate of 84% and an overall displaced joint detection rate of 94%. The code was also able to efficiently detect settled deposits in the pipelines with a detection rate of 90%. In addition, the automated ovality detection resulted in 100% compatibility with the manual circularity detection.