Self organising map based region of interest labelling for automated defect identification in large sewer pipe image collections

Proper maintenance of sewer pipes is vital for the healthy functioning of a city. Due to the difficulty of reach for sewage pipes, automating pipe inspection has high potential in providing an efficient and objective identification of defects which could lead to damaging the pipe system. A popular approach has been to send remote controlled robots to photograph the pipes and process the images to identify possible defects. However majority of the images contain regular pipe features such as the flow line, pipe joints and pipe connections. Regular features pose a challenge for automated defect detection algorithms which require high processing time. This paper proposes a self organising map based approach to leverage the regularity of image features to isolate regions of interest which could contain defects. As a result, the search space is narrowed down for the defect detection algorithms, decreasing the overall processing time. Novelty of the work lies in the feature extraction and the gradual isolation of the potential defective image features to a manageable size. Therefore, this technique is suitable for large scale real applications. We demonstrate the effectiveness of the proposed approach for a real pipe image data set.