本文提出了一种基于视觉的混凝土桥面裂缝检测方法，该检测方法将一维卷积神经网络（1D-CNN）与长短期记忆（LSTM）方法集成在图像频域。1D-CNN-LSTM算法经过了数千张开裂以及非开裂的混凝土桥面图像的训练。为了提高训练效率，在预处理阶段，图像首先会被转换到频域，然后使用扁平化的频率数据对算法进行校准。LSTM则被用来提高针对长序列数据开发的网络的性能。所开发模型的训练、验证与测试数据的准确率分别为99.05%、98.9%和99.25%。随后，本文进一步提出了一个运行框架，以便在未来将经过训练的模型应用于大尺寸图像。与现有的深度学习方法相比，本文所提出的1D-CNN-LSTM算法在准确度与计算时间等方面均有卓越的表现。可以预见，1D-CNN-LSTM算法的快速运行使其在实时裂缝检测领域必将大有可为。

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.