Scholarlink

Crack detection plays a crucial role in ensuring the safety and integrity of various structures, including buildings, bridges, and roadways. Convolutional Neural Networks (CNNs) have emerged as powerful tools for automated crack detection, offering the potential for accurate and efficient analysis. In this review paper, we conduct a brief analysis of CNN architectures employed for crack detection, focusing on their comparative performance, application domains, and future research directions. By examining a collection of 20 relevant studies, we identify and evaluate the utilization frequency of popular CNN architectures, namely AlexNet, GoogleNet, ResNet, VGG-16, YOLO v3, and YOLO v4, in the context of crack detection. This review offers valuable insights into the prevalence, usage patterns, and performance of CNN architectures in crack detection. It serves as a guide for researchers and practitioners in developing effective CNN-based crack detection systems.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

A Review of Detecting and Quantification of Cracks Using Convolutional Neural Networks and Image Processing Techniques

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Shaikha H. Mokhlis,
  • Alanoud A. Alhomoud,
  • Reema I. Alshawi,
  • Shatha K. Alhazzani,
  • Lamees A. Alhazzaa

摘要

Crack detection plays a crucial role in ensuring the safety and integrity of various structures, including buildings, bridges, and roadways. Convolutional Neural Networks (CNNs) have emerged as powerful tools for automated crack detection, offering the potential for accurate and efficient analysis. In this review paper, we conduct a brief analysis of CNN architectures employed for crack detection, focusing on their comparative performance, application domains, and future research directions. By examining a collection of 20 relevant studies, we identify and evaluate the utilization frequency of popular CNN architectures, namely AlexNet, GoogleNet, ResNet, VGG-16, YOLO v3, and YOLO v4, in the context of crack detection. This review offers valuable insights into the prevalence, usage patterns, and performance of CNN architectures in crack detection. It serves as a guide for researchers and practitioners in developing effective CNN-based crack detection systems.