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Concrete Surface Crack Detection with Convolutional-based Deep Learning Models
  • +1
  • farhad kooban,
  • Sara Shomal Zadeh,
  • Sina Aalipour Birgani,
  • Meisam Khorshidi
farhad kooban
Department of Civil and Environmental Engineering, Lamar University

Corresponding Author:[email protected]

Author Profile
Sara Shomal Zadeh
Department of Civil and Environmental Engineering, Lamar University
Sina Aalipour Birgani
Master of Mechanical engineering -energy conversion, Sharif university of technology international campus kish island
Meisam Khorshidi
Department of Civil and Environmental Engineering, University of New Hampshire


Effective crack detection is pivotal for the structural health monitoring and inspection of buildings. This task presents a formidable challenge to computer vision techniques due to the inherently subtle nature of cracks, which often exhibit low-level features that can be easily confounded with background textures, foreign objects, or irregularities in construction. Furthermore, the presence of issues like non-uniform lighting and construction irregularities poses significant hurdles for autonomous crack detection during building inspection and monitoring. Convolutional neural networks (CNNs) have emerged as a promising framework for crack detection, offering high levels of accuracy and precision. Additionally, the ability to adapt pretrained networks through transfer learning provides a valuable tool for users, eliminating the need for an in-depth understanding of algorithm intricacies. Nevertheless, it is imperative to acknowledge the limitations and considerations when deploying CNNs, particularly in contexts where the outcomes carry immense significance, such as crack detection in buildings. In this paper, our approach to surface crack detection involves the utilization of various deep learning models. Specifically, we employ fine-tuning techniques on pre-trained deep learning architectures: VGG19, ResNet50, Inception V3, and EfficientNetV2. These models are chosen for their established performance and versatility in image analysis tasks. We compare deep learning models using precision, recall, and F1 score.