PIPE-CovNet: Automatic In-Pipe Wastewater Infrastructure Surface Abnormality Detection Using Convolutional Neural Network
- Publisher:
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Publication Type:
- Journal Article
- Citation:
- IEEE Sensors Letters, 2023, 7, (4)
- Issue Date:
- 2023-04-01
Closed Access
| Filename | Description | Size | |||
|---|---|---|---|---|---|
| PIPE-CovNet_Automatic_In-Pipe_Wastewater_Infrastructure_Surface_Abnormality_Detection_Using_Convolutional_Neural_Network.pdf | Published version | 1.3 MB |
Copyright Clearance Process
- Recently Added
- In Progress
- Closed Access
This item is closed access and not available.
Regular inspection of multibillion dollar wastewater pipe infrastructure is crucial to any city around the globe. Traditional processes of inspection are laborious, time-consuming, and prone to human errors, such as the manual assessment of video and image sources obtained by closed-circuit television (CCTV). These limitations can be circumvented through the utilization of novel deep learning techniques. In this letter, we propose the PIPE-CovNet model, leveraging a convolutional neural network for automatic pipe surface abnormality detection. The proposed deep learning framework was trained and evaluated on a publicly accessible dataset. Evaluation results indicate the PIPE-CovNet achieves 82% accuracy and F1-score 0.82. In addition, the PIPE-CovNet outperformed other comparable deep learning models in terms of accuracy by at least 5% and F1-score by at minimum 8%.
Please use this identifier to cite or link to this item: