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Continuous monitoring and real-time analysis are crucial for reducing the number of driving-related incidents. Every day, road surfaces experience wear and develop cracks, necessitating regular inspections. An extensive analysis of various methods for crack detection, recognition and identification was conducted, emphasizing the efficiency of deep convolutional neural networks in crack classification. There is no single model that integrates all the relevant approaches. This review aims to provide a list of models that can be beneficial to researchers in this area.

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A Crevice-Oriented Strategy for Surface Crack Identification

  • 会议论文

  • 发表日期：

    2025年

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  • Prema Sahane,
  • Nivedita Shimbre,
  • Upendra Chandra Maurya,
  • Rohit Bamble,
  • Akanksha Daware,
  • Vaishnavi Kumbhar

摘要

Continuous monitoring and real-time analysis are crucial for reducing the number of driving-related incidents. Every day, road surfaces experience wear and develop cracks, necessitating regular inspections. An extensive analysis of various methods for crack detection, recognition and identification was conducted, emphasizing the efficiency of deep convolutional neural networks in crack classification. There is no single model that integrates all the relevant approaches. This review aims to provide a list of models that can be beneficial to researchers in this area.