论文标题
通过基于跨层图融合模块的双重任务网络进行道路检测
Road detection via a dual-task network based on cross-layer graph fusion modules
论文作者
论文摘要
基于遥感图像的道路检测对于智能交通管理至关重要。主流道路检测方法的性能主要取决于其提取的特征，它们的丰富性和稳健性可以通过融合不同类型和跨层连接的特征来增强。但是，现有主流模型框架中的功能通常在同一层中通过单任务训练相似，而传统的跨层融合方式太简单了，无法获得有效的效果，因此除了串联外，还应该探索更复杂的融合方式。针对上述缺陷，我们提出了一个双重任务网络（DTNET），用于道路检测和跨层图融合模块（CGM）：DTNET分别由两个平行分支组成，分别用于道路区域和边缘检测，同时通过我们设计的特征桥梁模块（FBM）在两个分支之间融合了特征多样性（FBM）。 CGM通过复杂的特征流图改善了跨层融合效果，并评估了四个图模式。三个公共数据集的实验结果表明，我们的方法有效地改善了最终检测结果。
Road detection based on remote sensing images is of great significance to intelligent traffic management. The performances of the mainstream road detection methods are mainly determined by their extracted features, whose richness and robustness can be enhanced by fusing features of different types and cross-layer connections. However, the features in the existing mainstream model frameworks are often similar in the same layer by the single-task training, and the traditional cross-layer fusion ways are too simple to obtain an efficient effect, so more complex fusion ways besides concatenation and addition deserve to be explored. Aiming at the above defects, we propose a dual-task network (DTnet) for road detection and cross-layer graph fusion module (CGM): the DTnet consists of two parallel branches for road area and edge detection, respectively, while enhancing the feature diversity by fusing features between two branches through our designed feature bridge modules (FBM). The CGM improves the cross-layer fusion effect by a complex feature stream graph, and four graph patterns are evaluated. Experimental results on three public datasets demonstrate that our method effectively improves the final detection result.
