Automatic Pavement Crack Detection Based on Octave Convolution Neural Network with Hierarchical Feature Learning

Minggang Xu, Chong Li*, Ying Chen, Wu Wei

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

Automatic pavement crack detection plays an important role in ensuring road safety. In images of cracks, information about the cracks can be conveyed through high-frequency and low-frequency signals that focus on fine details and global structures, respectively. The output features obtained from different convolutional layers can be combined to represent information about both high-frequency and low-frequency signals. In this paper, we propose an encoder-decoder framework called octave hierarchical network (Octave-H), which is based on the U-Network (U-Net) architecture and utilizes an octave convolutional neural network and a hierarchical feature learning module for performing crack detection. The proposed octave convolution is capable of extracting multi-frequency feature maps, capturing both fine details and global cracks. We propose a hierarchical feature learning module that merges multi-frequency-scale feature maps with different levels (high and low) of octave convolutional layers. To verify the superiority of the proposed Octave-H, we employed the CrackForest dataset (CFD) and AigleRN databases to evaluate this method. The experimental results demonstrate that Octave-H outperforms other algorithms with satisfactory performance.

Original languageEnglish
Pages (from-to)422-435
Number of pages14
JournalJournal of Beijing Institute of Technology (English Edition)
Volume33
Issue number5
DOIs
Publication statusPublished - 2024

Keywords

  • automated pavement crack detection
  • hierarchical feature
  • multifrequency
  • multiscale
  • octave convolutional network

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