ABSTRACT
Up until recently, inspection of sewer pipes has been a challenging task. The reason is that 95% of this class of pipes is too small for effective manual (i.e. walk-in) inspection. The need to assess the condition of sewer pipes gave rise to the development of new techniques for inspection. In an effort to develop new techniques, the closed circuit television (CCTV) camera was first introduced in the 1960s. The process of manual CCTV inspection is usually time consuming, tedious and expensive. It may also lead to diagnostic errors due to lack of concentration of human inspectors. This paper briefly describes a recently developed inspection system that detects and classifies defects automatically in sewer pipes, and focuses primarily on presenting an effective computer-based model, designed to verify the output of the developed system. The model utilizes multiple neural networks and data processing strategy to perform its task. A case example is presented to demonstrate the use and capabilities of the developed model.
