Simultaneous pixel-level detection of multiple distresses and surface design features on complex asphalt pavements is a critical challenge in intelligent pavement survey. This paper proposes a deep-learning model named ShuttleNet to provide an efficient solution for this challenge by implementing robust semantic segmentation on asphalt pavements. The proposed ShuttleNet aims at repeating the encoding–decoding round freely or even endlessly such that the contexts at different resolution levels can be learned and integrated many times for enhanced latent representations. Additionally, a new and efficient connection method called memory connection is also proposed in the paper and deployed in the ShuttleNet model to provide shortcut connections between successive encoding–decoding rounds. The proposed memory connection can partially or entirely carry the decoded information at different resolution levels into the next encoding–decoding round. Pairing 3D pavement images with 2D pavement images, the proposed ShuttleNet model is applied to detect multiple distresses and surface design features on asphalt pavements simultaneously, including pavement cracks, potholes, sealed cracks, patches, markings, expansion joints, and the pavement background. Experimental results demonstrate that the mean F-measure and mean intersection-over-union attained by the recommended architectural variation of the proposed ShuttleNet model on 1500 testing image pairs are 92.54% and 0.8657 respectively. According to the performance comparisons using both private and public datasets, the proposed ShuttleNet model can yield a noticeably higher detection accuracy, compared with four state-of-the-art models for semantic segmentation.

中文翻译：

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沥青路面上多种故障和表面设计特征的智能像素级检测

在复杂的沥青路面上同时检测多个故障和表面设计特征的像素级是智能路面测量的关键挑战。本文提出了一种名为 ShuttleNet 的深度学习模型，通过在沥青路面上实现鲁棒的语义分割，为这一挑战提供有效的解决方案。所提出的 ShuttleNet 旨在自由甚至无休止地重复编码 - 解码轮次，以便可以多次学习和集成不同分辨率级别的上下文以增强潜在表示。此外，论文还提出了一种新的高效连接方法，称为内存连接，并部署在 ShuttleNet 模型中，以提供连续编码-解码轮之间的快捷连接。所提出的内存连接可以部分或全部携带不同分辨率级别的解码信息进入下一轮编码-解码。将 3D 路面图像与 2D 路面图像配对，将所提出的 ShuttleNet 模型应用于同时检测沥青路面上的多个破损和表面设计特征，包括路面裂缝、坑洼、密封裂缝、补丁、标记、伸缩缝和路面背景。实验结果表明，建议的 ShuttleNet 模型在 1500 个测试图像对上的推荐架构变化所获得的平均 F 度量和平均交叉联合分别为 92.54% 和 0.8657。根据使用私有和公共数据集的性能比较，