Buatik Apichat, Thansirichaisree Phromphat, Kalpiyapun Phisutwat, Khademi Navid, Pasityothin Ittipon, Poovarodom Nakhorn
Research Unit of Infrastructure Inspection, Monitoring, Repair and Strengthening, Faculty of Engineering, Thammasat School of Engineering, Thammasat University, Pathumthani, Thailand.
School of Civil Engineering, College of Engineering, University of Tehran, Tehran, Iran.
Sci Rep. 2024 Apr 3;14(1):7851. doi: 10.1038/s41598-024-58432-w.
Cracks are the primary indicator informing the structural health of concrete structures. Frequent inspection is essential for maintenance, and automatic crack inspection offers a significant advantage, given its efficiency and accuracy. Previously, image-based crack detection systems have been utilized for individual images, yet these systems are not effective for large inspection areas. This paper thereby proposes an image-based crack detection system using a Deep Convolution Neural Network (DCNN) to identify cracks in mosaic images composed from UAV photos of concrete footings. UAV images are transformed into 3D footing models, from which the composite images are created. The CNN model is trained on 224 224 pixel patches, and training samples are augmented by various image transformation techniques. The proposed method is applied to localize cracks on composite images through the sliding window technique. The proposed VGG16 CNN detection system, with 95% detection accuracy, indicates superior performance to feature-based detection systems.
裂缝是反映混凝土结构健康状况的主要指标。定期检查对于维护至关重要，而自动裂缝检测因其效率和准确性具有显著优势。此前，基于图像的裂缝检测系统已用于单个图像，但这些系统对大面积检测区域效果不佳。因此，本文提出一种基于图像的裂缝检测系统，该系统使用深度卷积神经网络（DCNN）来识别由混凝土基础的无人机照片组成的拼接图像中的裂缝。无人机图像被转换为三维基础模型，由此创建合成图像。CNN模型在224×224像素的图像块上进行训练，训练样本通过各种图像变换技术进行扩充。所提出的方法通过滑动窗口技术应用于在合成图像上定位裂缝。所提出的VGG16 CNN检测系统具有95%的检测准确率，表明其性能优于基于特征的检测系统。
