This paper presents a vision-based crack detection approach for concrete bridge decks using an integrated one-dimensional convolutional neural network (1D-CNN) and long short-term memory (LSTM) method in the image frequency domain. The so-called 1D-CNN-LSTM algorithm is trained using thousands of images of cracked and non-cracked concrete bridge decks. In order to improve the training efficiency, images are first transformed into the frequency domain during a preprocessing phase. The algorithm is then calibrated using the flattened frequency data. LSTM is used to improve the performance of the developed network for long sequence data. The accuracy of the developed model is 99.05%, 98.9%, and 99.25%, respectively, for training, validation, and testing data. An implementation framework is further developed for future application of the trained model for large-scale images. The proposed 1D-CNN-LSTM method exhibits superior performance in comparison with existing deep learning methods in terms of accuracy and computation time. The fast implementation of the 1D-CNN-LSTM algorithm makes it a promising tool for real-time crack detection.

本文提出了一种基于视觉的混凝土桥面裂缝检测方法，该方法使用图像频域中的集成一维卷积神经网络 (1D-CNN) 和长短期记忆 (LSTM) 方法。所谓的 1D-CNN-LSTM 算法是使用数千张开裂和非开裂混凝土桥面的图像进行训练的。为了提高训练效率，在预处理阶段首先将图像转换为频域。然后使用平坦的频率数据校准算法。LSTM 用于提高已开发网络对长序列数据的性能。所开发模型的训练、验证和测试数据的准确率分别为 99.05%、98.9% 和 99.25%。进一步开发了一个实现框架，以便将来将经过训练的模型应用于大规模图像。与现有的深度学习方法相比，所提出的 1D-CNN-LSTM 方法在准确性和计算时间方面表现出优越的性能。1D-CNN-LSTM 算法的快速实现使其成为实时裂纹检测的有前途的工具。