基于改进的MobileNetV3的轻质混凝土裂识别模型
Rui Wang1, Ruiqi Chen1, Hao Yan1
1College of Engineering, Sichuan Normal University, Chengdu, 610068, China.
Scientific reports
|May 5, 2025
概括
一个新的C//Sim注意力机制通过增强MobileNetV3.3来改善轻质混凝土裂检测. 这种强大的模型提供了更高的准确性和更少的参数,推进了混凝土裂识别研究.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 混凝土裂检测对于结构健康监测至关重要.
- 现有的轻量级模型在裂识别中可能缺乏准确性和稳定性.
- 注意力机制为改善图像分析中的深度学习模型提供了潜力.
研究的目的:
- 开发一个改进的轻质混凝土裂识别模型.
- 通过结合CA和SimAm的注意力来引入新的C//Sim注意力机制.
- 与现有方法对比,评估拟议模型的性能和稳定性.
主要方法:
- 该研究提出了C//Sim注意力机制,这是CA和SimAm注意力机制的并行连接.
- 移动网络V3被增强了C//Sim注意力机制,以创建移动网络V3-C//Sim模型.
- 该模型在自建和公共数据集上进行了训练和验证,用于破解检测.
主要成果:
- 与原来的MobileNetV3-C//Sim模型相比,MobileNetV3-C//Sim模型显示出更高的准确性,回忆,精度和F1得分,与原来的MobileNetV3.3相比.
- 注意力机制的并行连接被证明比单个机制或连接更有效.
- 在噪音下,C//Sim机制表现出最小的性能降低,表明强大的稳定性和噪音免疫力.
结论:
- 拟议的C//Sim注意力机制显著提高了轻质混凝土裂检测性能.
- 移动网络V3-C//Sim模型提供了更好的准确性,稳定性和参数效率.
- 这项研究为使用深度学习来识别轻质混凝土裂提供了有价值的参考.
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