Deep learning provides an efficient automated method for pavement condition surveys, but the datasets used for this model are usually images taken in good lighting conditions. If images are taken at night, this model cannot work effectively. This paper proposes a method for normalizing pavement images at night, which includes three main steps. First, the image feature point detection and matching method is used to process images taken during the day and night. Then, paired images of pavement during the day and night are obtained. Second, with the help of the image-to-image translation model, those paired images are used for training, and the best model for converting night images into day images is selected. Third, a convolutional neural network (CNN) based on VGGNet is constructed, and pavement images taken during the day are used for training. After that, six types of images are used and tested separately, namely, those taken during the day and the night, converted by the proposed method and converted by traditional methods. As evaluated by various evaluation indices and visualization methods, the detection performance of the CNN model can be significantly improved by using the proposed method of converted night-to-day images.

中文翻译：

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基于图像转换的夜间路面裂缝检测方法

深度学习为路面状况调查提供了一种高效的自动化方法，但用于该模型的数据集通常是在良好照明条件下拍摄的图像。如果在夜间拍摄图像，此模型无法有效工作。本文提出了一种在夜间对路面图像进行归一化的方法，包括三个主要步骤。首先，利用图像特征点检测与匹配方法对白天和夜间拍摄的图像进行处理。然后，获得白天和晚上的路面配对图像。其次，借助图像到图像的转换模型，将那些配对的图像用于训练，选择将夜间图像转换为白天图像的最佳模型。第三，构建基于VGGNet的卷积神经网络（CNN），使用白天拍摄的路面图像进行训练。之后，分别使用和测试了六种类型的图像，即白天和夜间拍摄的图像，用本文方法转换和用传统方法转换。通过各种评价指标和可视化方法的评价，CNN模型的检测性能可以通过使用所提出的转换夜到日图像的方法得到显着提高。