Crack detection using image processing has recently become a major research topic in nondestructive inspection (NDT) and structural health monitoring (SHM). However, crack detection methods are not robust to variations such as illumination, weather, and noise, due to the speed and accuracy of stitching cannot meet the requirements of practical applications. Therefore, an automated crack detection system built basing on deep learning to replace manual visual inspection. This article presents a SIFT matching method based on an alternate-selection strategy to eliminate residual reprojection and geometric distortion errors. A variety of network models based on the fully convolutional network (FCN) architecture are proposed to address the problems of local information losses and partial refinement capacity reductions for improving detection accuracy and reduce the error mark under a complex background, which are frequently encountered in the crack detection algorithms of deep learning methods. Combining pretraining with fine-tuning to verify the detection performance of FCN-based networks and use the feature descriptor to determine the location and size of each crack. Verifing the robustness of the proposed approach for the steel-beam crack detection task on the crack image dataset by an extensive experimental evaluation.

使用图像处理进行裂纹检测最近已成为无损检测 (NDT) 和结构健康监测 (SHM) 的主要研究课题。然而，由于拼接的速度和精度不能满足实际应用的要求，裂纹检测方法对光照、天气和噪声等变化不具有鲁棒性。因此，基于深度学习构建的自动化裂纹检测系统来取代人工目视检查。本文提出了一种基于交替选择策略的 SIFT 匹配方法，以消除残差重投影和几何失真误差。提出了多种基于全卷积网络（FCN）架构的网络模型来解决局部信息丢失和局部细化容量降低的问题，以提高检测精度并减少复杂背景下的错误标记，这些问题在复杂背景下是经常遇到的。深度学习方法的裂纹检测算法。结合预训练和微调来验证基于 FCN 的网络的检测性能，并使用特征描述符来确定每个裂缝的位置和大小。通过广泛的实验评估验证所提出的方法在裂纹图像数据集上进行钢梁裂纹检测任务的鲁棒性。结合预训练和微调来验证基于 FCN 的网络的检测性能，并使用特征描述符来确定每个裂缝的位置和大小。通过广泛的实验评估验证所提出的方法在裂纹图像数据集上进行钢梁裂纹检测任务的鲁棒性。结合预训练和微调来验证基于 FCN 的网络的检测性能，并使用特征描述符来确定每个裂缝的位置和大小。通过广泛的实验评估验证所提出的方法在裂纹图像数据集上进行钢梁裂纹检测任务的鲁棒性。