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<p>This study created the C//Sim attention mechanism employing the parallel connection of the CA attention mechanism and the SimAm attention mechanism to detect cracks in lightweight concrete. MobileNetV3 was improved using the above method, and a lightweight concrete crack recognition model, MobileNetV3-C//Sim, was established. To validate the model’s practicality, this paper has been tested on self-built and public datasets. The improved model performs higher accuracy, recall, precision, and F1 values than Mobilenetv3 in both datasets, with increases of 0.44–0.69% and 0.46–0.89% for the binary and multi classification tasks, respectively. For the CA attention mechanism, SimAm attention mechanism, and ablation tests with different combinations of each other showed that the parallel connection combination was superior to the single-type, front-to-back concatenation combination. In noise testing with different attention mechanisms, the C//Sim reduction is the smallest. It is verified to have better noise immunity and robustness. Regarding the number of model parameters, the proposed method involves only 2.90&#xa0;M, which is 30.17% less than that of MobileNetV3. The method can provide a model reference for further concrete crack lightweight identification research.</p>

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Lightweight concrete crack recognition model based on improved MobileNetV3

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月5日

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  • Rui Wang,
  • Ruiqi Chen,
  • Hao Yan,
  • Xinxin Guo

摘要

This study created the C//Sim attention mechanism employing the parallel connection of the CA attention mechanism and the SimAm attention mechanism to detect cracks in lightweight concrete. MobileNetV3 was improved using the above method, and a lightweight concrete crack recognition model, MobileNetV3-C//Sim, was established. To validate the model’s practicality, this paper has been tested on self-built and public datasets. The improved model performs higher accuracy, recall, precision, and F1 values than Mobilenetv3 in both datasets, with increases of 0.44–0.69% and 0.46–0.89% for the binary and multi classification tasks, respectively. For the CA attention mechanism, SimAm attention mechanism, and ablation tests with different combinations of each other showed that the parallel connection combination was superior to the single-type, front-to-back concatenation combination. In noise testing with different attention mechanisms, the C//Sim reduction is the smallest. It is verified to have better noise immunity and robustness. Regarding the number of model parameters, the proposed method involves only 2.90 M, which is 30.17% less than that of MobileNetV3. The method can provide a model reference for further concrete crack lightweight identification research.