PIPE-CovNet : automatic in-pipe wastewater infrastructure surface abnormality detection using convolutional neural network

X. Wang, Karthick Thiyagarajan, S. Kodagoda, M. Zhang

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)

Abstract

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%.
Original languageEnglish
Article number6001904
Number of pages4
JournalIEEE Sensors Letters
Volume7
Issue number4
DOIs
Publication statusPublished - 1 Apr 2023

Keywords

  • automatic defect detection
  • deep learning
  • infrastructure robotics
  • robot vision
  • pipe robotics
  • Sensor applications

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