基于深度学习的石混凝土储面板裂的图像识别技术.
Kai Hu1,2, Yang Ling2, Jie Liu3
1School of Civil Engineering and Architecture, Xi'an Technological University, Xi 'an, Shaanxi, China.
PloS one
|February 4, 2025
概括
本研究介绍了一种先进的深度学习异常模型,用于检测青混凝土裂. 这种新的方法显著提高了准确性和稳定性,即使在具有挑战性的环境条件下.
科学领域:
- 土木工程 土木工程是指土木工程.
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 由于环境因素影响图像质量和精度,青混凝土的裂检测受到阻碍.
- 现有的方法难以应对照明,反射和天气的变化.
研究的目的:
- 引入一种基于深度学习的新型异常模型,用于在青混凝土中准确和强大的裂检测.
- 增强特征提取,权重和信息传输,以提高检测性能.
主要方法:
- 采集并处理了大量的面板图像数据集,使用无声化,标准化和数据增强.
- 开发了一种改进的Xception网络,包含了自适应激活功能,动态注意力机制和多级剩余连接.
- 使用LabelImg软件精确标记裂区域.
主要成果:
- 增强的深度学习模型实现了97.6%的准确性和0.98.98的马修斯相关系数.
- 在不同的照明条件下证明了稳定的性能和高精度.
- 显著改善了特征提取,权重和信息传输能力.
结论:
- 新的深度学习异常模型在青混凝土裂检测方面取得了重大进展.
- 拟议的方法提高了检测准确性,稳定性和效率,克服了环境挑战.
- 这种方法为基础设施监控和维护提供了有价值的工具.
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