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A Pipeline Defect Instance Segmentation System Based on SparseInst.

Niannian Wang1, Jingzheng Zhang1, Xiaotian Song2

  • 1School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou 450001, China.

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|November 25, 2023
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Summary

Pipe-Sparse-Net, a new system using StyleGAN3 and SparseInst, accurately segments underground drainage pipe defects. This deep learning approach enhances defect detection speed and precision for infrastructure inspection.

Keywords:
data augmentationdeep learningimage segmentationpipeline defects

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Infrastructure Engineering

Background:

  • Deep learning shows promise for pipeline defect segmentation.
  • Current methods struggle with complex defect features and slow processing speeds.

Purpose of the Study:

  • To develop an advanced pipeline defect segmentation system.
  • To improve accuracy and processing speed for underground drainage pipe defect detection.

Main Methods:

  • Utilized StyleGAN3 for data augmentation to expand the dataset.
  • Developed Pipe-Sparse-Net, a segmentation model based on SparseInst.
  • Integrated StyleGAN3 with SparseInst for complex defect segmentation.

Main Results:

  • Achieved 91.4% segmentation accuracy.
  • Reached a processing speed of 56.7 frames per second (FPS).
  • Outperformed Yolact, Condinst, and Mask R-CNN with a 45% speed increase and over 4% accuracy improvement.

Conclusions:

  • Pipe-Sparse-Net effectively segments complex pipeline defects.
  • The proposed system offers significant improvements in both accuracy and speed over existing methods.
  • This approach has strong potential for enhancing infrastructure inspection and maintenance.