Wang Rui, Chen Ruiqi, Yan Hao, Guo Xinxin
College of Engineering, Sichuan Normal University, Chengdu, 610068, China.
State Key Laboratory of Geohazard Prevention and Geoenvironmental Protection, Chengdu University of Technology, Chengdu, 610059, China.
Sci Rep. 2025 May 5;15(1):15704. doi: 10.1038/s41598-025-00468-7.
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.
本研究创建了C//Sim注意力机制，采用CA注意力机制和SimAm注意力机制的并行连接来检测轻量混凝土中的裂缝。使用上述方法对MobileNetV3进行了改进，并建立了轻量混凝土裂缝识别模型MobileNetV3-C//Sim。为验证该模型的实用性，本文在自建数据集和公开数据集上进行了测试。在两个数据集中，改进后的模型在准确率、召回率、精确率和F1值方面均高于MobileNetv3，二分类任务和多分类任务的提升分别为0.44 - 0.69%和0.46 - 0.89%。对于CA注意力机制、SimAm注意力机制以及它们相互之间不同组合的消融测试表明，并行连接组合优于单类型、前后拼接组合。在不同注意力机制的噪声测试中，C//Sim的降幅最小。经验证，其具有更好的抗噪声能力和鲁棒性。关于模型参数数量，所提方法仅涉及290万个参数，比MobileNetV3少30.17%。该方法可为进一步的混凝土裂缝轻量识别研究提供模型参考。
