受到天窗时间和自然环境的影响，传统的裂缝图像检测方法容易混淆轨道板裂缝与复杂的背景噪声，轨道板裂缝图片样本数量也不能满足训练模型的要求，最终导致检测错误和遗漏。为了提高复杂环境和小样本条件下轨道板裂缝检测效率，提出了一种基于卷积神经网络和生成对抗网络的轨道板裂缝识别模型。首先，制作轨道板裂缝数据集，引入循环生成对抗网络增强采集的轨道板裂缝病害图像数据集。然后，通过在ResNet50网络基础上加入扩张卷积和坐标注意力机制模块，加深网络层数获取更丰富的图像特征，提升网络对关键特征的学习能力。最后，将改进ResNet50网络模型，并与现有的三种深度卷积神经网络模型进行性能比较，其准确率、精确度、召回率和F₁分数分别为93.51%、93.44%、93.46%、93.45%，均高于其他模型。该模型有效解决轨道板裂缝图像缺乏、图像背景复杂等问题，同时检测的结果满足实际轨道板裂缝检修作业的需求，可为轨道结构检修人员提供参考。

Due to the influence of the skylight time and the natural environment, the traditional crack detection method is easy to confuse the cracks of the track plate with complex background noises. The number of crack image samples of the track plate is often unable to meet the requirements of the training model. It eventually leads to detection errors and omissions. In order to improve the efficiency of crack detection for track plate in complex environment and small samples, a crack detection model of track plate based on convolutional neural network and generated adversarial network was proposed. Firstly, the data set of the track plate crack was propuced. The image data set of the track plate crack disease was enhanced by cycle generative adversarial network. Then, the expansion convolution and coordinate attention mechanism modules were added to the ResNet50 network to deepen the network layer. It can obtain more abundant image features and enhance network's ability to learn key features. Finally, the performance of the improved ResNet50 network model was compared with three existing depth convolutional neural network models. Its accuracy, precision, recall, and F₁ score are 93.51%, 93.44%, 93.46%, and 93.45%, respectively, which are all higher than other models. The model effectively solves the problems such as lack of track plate crack image and complex image background. The detection results meet the needs of actual track plate crack repair operations, which can provide reference for track structure maintenance personnel.