Centre for Nonlinear Systems, Department of Mathematical Modelling, Kaunas University of Technology, 51368 Kaunas, Lithuania.
Sensors (Basel). 2022 May 11;22(10):3662. doi: 10.3390/s22103662.
Deep learning-based methods, especially convolutional neural networks, have been developed to automatically process the images of concrete surfaces for crack identification tasks. Although deep learning-based methods claim very high accuracy, they often ignore the complexity of the image collection process. Real-world images are often impacted by complex illumination conditions, shadows, the randomness of crack shapes and sizes, blemishes, and concrete spall. Published literature and available shadow databases are oriented towards images taken in laboratory conditions. In this paper, we explore the complexity of image classification for concrete crack detection in the presence of demanding illumination conditions. Challenges associated with the application of deep learning-based methods for detecting concrete cracks in the presence of shadows are elaborated on in this paper. Novel shadow augmentation techniques are developed to increase the accuracy of automatic detection of concrete cracks.
基于深度学习的方法，特别是卷积神经网络，已经被开发出来以自动处理混凝土表面的图像，用于裂缝识别任务。尽管基于深度学习的方法声称具有非常高的准确性，但它们往往忽略了图像采集过程的复杂性。真实世界的图像通常受到复杂的照明条件、阴影、裂缝形状和大小的随机性、瑕疵和混凝土剥落的影响。已发表的文献和可用的阴影数据库都针对在实验室条件下拍摄的图像。在本文中，我们探讨了在具有挑战性的照明条件下进行混凝土裂缝检测的图像分类的复杂性。本文详细阐述了在存在阴影的情况下应用基于深度学习的方法检测混凝土裂缝所面临的挑战。开发了新的阴影增强技术来提高混凝土裂缝自动检测的准确性。
