Smart City Gnosys
Smart city article details
| Title | Water Pipe Valve Detection By Using Deep Neural Networks |
|---|---|
| ID_Doc | 61491 |
| Authors | Rayhana R.; Jiao Y.; Liu Z.; Wu A.; Kong X. |
| Year | 2020 |
| Published | Proceedings of SPIE - The International Society for Optical Engineering, 11382 |
| DOI | http://dx.doi.org/10.1117/12.2558886 |
| Abstract | Condition assessment of underground buried utilities, especially water distribution networks, is crucial to the decision making process for pipe replacement and rehabilitation. Hence, regular inspection of the water pipelines is carried out with in-pipe inspection robots to assess the internal condition of the water pipelines. However, the inspection robots need to identify and negotiate with the valves to pass through. Therefore, the aim of this study is to detect the valves in water pipelines in real-time to ensure smooth operation of the inspection robot. In this paper, four state-of-the-art deep neural network algorithms namely, Faster R-CNN, RFCN, SSD, and YOLO are presented to perform the real-time valve detection analysis. The study shows that Faster R-CNN, pre-trained with Resnet101 outperforms all the selected models by achieving 97:35% and 76:73% mean Average precison (mAP) values when the threshold for prediction is set to 50% and 75% respectively. However, in terms of the detection rate in frames per second (FPS), YOLOv3-608 seems to have better processing speed than all other models. © 2020 SPIE. |
| Author Keywords | Computer vision; Deep neural networks; Inspection; Valve detection; Water pipelines |
Similar Articles
| Id | Similarity | Authors | Title | Published |
|---|---|---|---|---|
1344  | 0.865 | Kumar S.S.; Abraham D.M. | A Deep Learning Based Automated Structural Defect Detection System For Sewer Pipelines | Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019 (2019) |
| 1345 | 0.862 | Khan S.M.; Haider S.A.; Unwala I. | A Deep Learning Based Classifier For Crack Detection With Robots In Underground Pipes | HONET 2020 - IEEE 17th International Conference on Smart Communities: Improving Quality of Life using ICT, IoT and AI (2020) |
| 44423 | 0.856 | Saleem F.; Ahmad Z.; Kim J.-M. | Real-Time Pipeline Leak Detection: A Hybrid Deep Learning Approach Using Acoustic Emission Signals | Applied Sciences (Switzerland), 15, 1 (2025) |