Accurate semantic segmentation ground-truths are difficult and expensive to obtain. On the other hand, the most promising approaches to automatically tackle this task, i.e. Deep Convolutional Neural Networks (CNNs), require high volumes of labeled data. We propose a new method based on deep learning for data augmentation in the context of semantic segmentation of highly-textured images. The method exploits a Generative Adversarial Network (GAN) to produce a semantic layout, then a texture synthesizer, based on a CNN, generates a new image according to the generated semantic layout and a reference real image taken from the training set. Even though our method is general and it can be utilized on a broad set of problems, we employed it on the real-world problem of detecting and localizing defects and cracks in road asphalts. We show how, starting from few labeled images, it is possible to augment small and long-tail datasets by producing new images with the associated semantic layouts. We prove the effectiveness of our approach by evaluating the performance of three different CNNs for semantic segmentation on the German Pavement Distress dataset and on a novel asphalt dataset collected by us. Results show a remarkable increase in performance, especially with low cardinality classes, when CNNs are trained on the augmented datasets with respect to original datasets.

准确的语义分段真相很难获得且昂贵。另一方面，自动解决此任务的最有前途的方法，即深度卷积神经网络（CNN），需要大量的标记数据。我们提出了一种基于深度学习的新方法，用于在高纹理图像的语义分割的背景下进行数据扩充。该方法利用生成对抗网络（GAN）生成语义布局，然后基于CNN的纹理合成器根据生成的语义布局和从训练集中获取的参考真实图像生成新图像。尽管我们的方法是通用的，并且可以用于广泛的问题，但我们还是将其用于检测和定位道路沥青中的缺陷和裂缝的实际问题。我们展示如何 从少量带标签的图像开始，可以通过生成具有相关语义布局的新图像来扩充小型和长尾数据集。我们通过评估三种不同的CNN在德国路面遇险数据集和我们收集的新型沥青数据集上进行语义分割的性能，证明了该方法的有效性。结果显示，当在增强数据集上相对于原始数据集训练CNN时，尤其是对于低基数类，性能显着提高。我们通过评估三种不同的CNN在德国路面遇险数据集和我们收集的新型沥青数据集上进行语义分割的性能，证明了该方法的有效性。结果显示，当在增强数据集上相对于原始数据集训练CNN时，尤其是对于低基数类，性能显着提高。我们通过评估三种不同的CNN在德国路面遇险数据集和我们收集的新型沥青数据集上进行语义分割的性能，证明了该方法的有效性。结果显示，当在增强数据集上相对于原始数据集训练CNN时，尤其是对于低基数类，性能显着提高。