论文标题
TB-NET：一个三潮型边界感知网络，用于细粒度的路面疾病分割
TB-Net: A Three-Stream Boundary-Aware Network for Fine-Grained Pavement Disease Segmentation
论文作者
论文摘要
定期的路面检查在维护安全保证方面起着重要作用。现有方法主要解决仅针对长期裂纹疾病量身定制的裂纹检测和细分任务。但是，还有许多其他类型的疾病，具有多种多样的尺寸和模式，对于在实践中进行细分也是必不可少的，为细粒度的人行道检查带来了更多挑战。在本文中，我们的目标不仅是自动分割裂缝，还要分割其他复杂的路面疾病以及典型的地标（标记，跑道灯等），并在单个模型中通常看到水/油污渍。为此，我们提出了一个三流的边界感知网络（TB-NET）。它由三个流组成，融合了低级空间和高级上下文表示以及详细的边界信息。具体而言，空间流捕获了丰富的空间特征。利用注意力机制的上下文流对本地特征的上下文关系进行了建模。边界流使用全球门控卷积学习详细的边界，以进一步完善分割输出。该网络以端到端的方式使用双重任务丢失进行了训练，并且对新收集的细粒型路面疾病数据集进行了实验，显示了我们TB-NET的有效性。
Regular pavement inspection plays a significant role in road maintenance for safety assurance. Existing methods mainly address the tasks of crack detection and segmentation that are only tailored for long-thin crack disease. However, there are many other types of diseases with a wider variety of sizes and patterns that are also essential to segment in practice, bringing more challenges towards fine-grained pavement inspection. In this paper, our goal is not only to automatically segment cracks, but also to segment other complex pavement diseases as well as typical landmarks (markings, runway lights, etc.) and commonly seen water/oil stains in a single model. To this end, we propose a three-stream boundary-aware network (TB-Net). It consists of three streams fusing the low-level spatial and the high-level contextual representations as well as the detailed boundary information. Specifically, the spatial stream captures rich spatial features. The context stream, where an attention mechanism is utilized, models the contextual relationships over local features. The boundary stream learns detailed boundaries using a global-gated convolution to further refine the segmentation outputs. The network is trained using a dual-task loss in an end-to-end manner, and experiments on a newly collected fine-grained pavement disease dataset show the effectiveness of our TB-Net.
