Automatic pavement distress detection is essential to monitoring and maintaining pavement condition. Currently, many deep learning-based methods have been utilized in pavement distress detection. However, distress segmentation remains as a challenge under complex pavement conditions. In this paper, a novel deep neural network architecture, W-segnet, based on multi-scale feature fusions, is proposed for pixel-wise distress segmentation. The proposed W-segnet concatenates distress location information with distress classification features in two symmetric encoder-decoder structures. Three major types of distresses: crack, pothole, and patch are segmented and the results were discussed. Experimental results show that the proposed W-segnet is robust in various scenarios, achieving a mean pixel accuracy (MPA) of 87.52% and a mean intersection over union (MIoU) of 75.88%. The results demonstrate that W-segnet outperforms other state-of-the-art semantic segmentation models of U-net, SegNet, and PSPNet. Comparison of cost of model training and inference indicates that W-segnet has the largest number of parameters, which needs a slightly longer training time while it does not increase the inference cost. Four public datasets were used to test the generalization ability of the proposed model and the results demonstrate that the W-segnet possesses well segmentation performance.

自动路面故障检测对于监测和维护路面状况至关重要。目前，许多基于深度学习的方法已被用于路面故障检测。然而，在复杂的路面条件下，遇险分割仍然是一个挑战。在本文中，提出了一种基于多尺度特征融合的新型深度神经网络架构 W-segnet，用于逐像素的窘迫分割。所提出的 W-segnet 在两个对称的编码器-解码器结构中将遇险位置信息与遇险分类特征连接起来。对三种主要类型的苦恼：裂缝、坑洞和补丁进行了分割，并讨论了结果。实验结果表明，所提出的 W-segnet 在各种场景下都具有鲁棒性，平均像素精度 (MPA) 为 87。52%，联合平均交集 (MIoU) 为 75.88%。结果表明，W-segnet 优于其他最先进的 U-net、SegNet 和 PSPNet 语义分割模型。模型训练和推理成本比较表明，W-segnet的参数数量最多，在不增加推理成本的情况下需要稍长的训练时间。四个公共数据集用于测试所提出模型的泛化能力，结果表明 W-segnet 具有良好的分割性能。在不增加推理成本的情况下需要稍长的训练时间。四个公共数据集用于测试所提出模型的泛化能力，结果表明 W-segnet 具有良好的分割性能。在不增加推理成本的情况下需要稍长的训练时间。四个公共数据集用于测试所提出模型的泛化能力，结果表明 W-segnet 具有良好的分割性能。