Name
Title | ||
|---|---|---|
Self organising map based region of interest labelling for automated defect identification in large sewer pipe image collections. |
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IJCNN.2012.6252482 | IJCNN |
Keywords | Field | DocType |
automatic optical inspection,edge detection,feature extraction,flaw detection,maintenance engineering,mechanical engineering computing,pipes,robot vision,sanitary engineering,self-organising feature maps,telerobotics,automated defect detection algorithm,automated defect identification,automated pipe inspection,defective image features isolation,feature extraction,flow line,image processing,large sewer pipe image collection,pipe connections,pipe joints,region of interest labelling,regular features,remote controlled robots,self-organising map,sewage pipes,sewer pipe maintenance,Sewer pipe defect identification,growing selforganising maps,hierarchical clustering | Computer vision,Feature (computer vision),Edge detection,Computer science,Flow line,Feature extraction,Artificial intelligence,Region of interest,Robot,Telerobotics,Maintenance engineering | Conference |
ISSN | Citations | PageRank |
2161-4393 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Hiran Ganegedara | 1 | 1 | 1.04 |
| Damminda Alahakoon | 2 | 334 | 50.54 |
| John Mashford | 3 | 72 | 10.75 |
| Andrew P. Paplinski | 4 | 0 | 0.34 |
| Karsten Muller | 5 | 14 | 10.32 |
| Thomas Martin Deserno | 6 | 530 | 67.07 |