基于值的BRISQUE辅助深度学习,用于增强混凝土结构中的裂纹检测
Sanjeetha Pennada1, Marcus Perry1, Jack McAlorum1
1Department of Civil and Environmental Engineering, University of Strathclyde, 75 Montrose St., Glasgow G1 1XJ, UK.
Journal of imaging
|October 27, 2023
概括
图像质量显著影响使用深度学习的裂检测. 较低的BRISQUE分数表明更好的图像质量,导致更高的精度识别混凝土裂和降低计算成本.
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:
- 混凝土结构的自动视觉检查系统依赖于精确的裂检测.
- 卷积神经网络 (CNN) 的性能受到低质量的图像的阻碍.
- 评估图像数据集的适用性对于可靠的深度学习模型至关重要.
研究的目的:
- 评估图像退化 (高斯噪声,模糊) 对使用VGG16.16的裂纹检测的影响.
- 探索图像质量指标 (BRISQUE) 和CNN性能之间的相关性.
- 为降低计算成本提出优化培训和测试的方法.
主要方法:
- 使用BRISQUE (盲人/无参考图像空间质量评估器) 方法来量化图像退化.
- 在数据集上训练和评估VGG16模型,数据集具有不同级别的高斯噪声和模糊.
- 分析了BRISQUE分数和裂分类指标 (精度,F1分数,MCC) 之间的关系.
主要成果:
- 在较低的BRISQUE分数和改善的裂分类性能之间发现了强烈的相关性.
- 更高的精度,F1得分和马修的相关系数 (MCC) 通过更少的图像降解来实现.
- 实施BRISQUE分数门可以优化模型培训和测试.
结论:
- 图像质量是CNN基于裂纹检测成功的一个关键因素.
- 在破裂检测的背景下,BRISQUE是评估图像质量的敏感指标.
- 使用BRISQUE值可以带来更高效和更具成本效益的结构健康监测的自动化视觉检查系统.
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