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Concrete Cracks Detection Using Convolutional NeuralNetwork Based on Transfer Learning

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  • Chao Su
  • Wenjun Wang

Abstract

Crack plays a critical role in the field of evaluating the quality of concrete structures, which affects the safety, applicability, and durability of the structure. Due to its excellent performance in image processing, the convolutional neural network is becoming the mainstream choice to replace manual crack detection. In this paper, we improve the EfficientNetB0 to realize the detection of concrete surface cracks using the transfer learning method. The model is designed by neural architecture search technology. The weights are pretrained on the ImageNet. Supervised learning uses Adam optimizer to update network parameters. In the testing process, crack images from different locations were used to further test the generalization capability of the model. By comparing the detection results with the MobileNetV2, DenseNet201, and InceptionV3 models, the results show that our model greatly reduces the number of parameters while achieving high accuracy (0.9911) and has good generalization capability. Our model is an efficient detection model, which provides a new option for crack detection in areas with limited computing resources.

Suggested Citation

  • Chao Su & Wenjun Wang, 2020. "Concrete Cracks Detection Using Convolutional NeuralNetwork Based on Transfer Learning," Mathematical Problems in Engineering, Hindawi, vol. 2020, pages 1-10, October.
  • Handle: RePEc:hin:jnlmpe:7240129
    DOI: 10.1155/2020/7240129
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    Cited by:

    1. Hina Inam & Naeem Ul Islam & Muhammad Usman Akram & Fahim Ullah, 2023. "Smart and Automated Infrastructure Management: A Deep Learning Approach for Crack Detection in Bridge Images," Sustainability, MDPI, vol. 15(3), pages 1-35, January.
    2. Luka Gradišar & Matevž Dolenc, 2023. "Transfer and Unsupervised Learning: An Integrated Approach to Concrete Crack Image Analysis," Sustainability, MDPI, vol. 15(4), pages 1-14, February.

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