Residual Dilated Attention for Semantic Segmentation of Traffic Scene Understanding
首发时间:2020-05-28
Abstract:In recent years, the convolutional neural network has achieved remarkable success in semantic segmentation of traffic scene understanding. At present, the main problems in the field of semantic segmentation are as follows: 1) The repeated pooling and downsampling operations reduce resolution of traffic images in the convolutional networks, which leads to lose abundant spatial information and poor segmentation performance. 2) Traffic images contain many objects of different scales. How to accurately recognize and segment these multi-scale objects is another key problem in semantic segmentation. To handle these problems, this paper propose an image semantic segmentation method based on the Residual Dilated Attention. This method uses spatial CNN to extract high-level semantic information, and then uses the proposed model to capture low-level semantic information, and follows the designed sampling rules to set appropriate and effective sampling rates, and effectively aggregates multi-scale context information while maintaining high resolution of feature maps. Finally, this paper also designs a fusion module to effectively fuse the results generated by the spatial CNN and the Residual Dilated Attention. The method in this paper conducts a series of simulation experiments on CULane and CamVid traffic datasets, and achieves competitive results, proving the effectiveness of the proposed method.
keywords: Computer vision Semantic segmentation Attention mechanism Dilated convolutions Multi-scale context information.
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面向交通场景理解的基于残差空洞注意力的语义分割方法
摘要:近年来,卷积神经网络在交通场景理解的语义分割方面取得了显著的成功。目前,语义分割领域存在的主要问题有:1)卷积网络中重复的池化和下采样操作降低了交通图像的分辨率,导致空间信息的丢失,其分割性能差。2)交通图像中包含了许多不同尺度的对象,如何准确识别和分割这些多尺度对象是另一个关键问题。针对这些问题,本文提出了一种基于残差空洞注意力的图像语义分割方法。该方法使用空间CNN来提取高级语义信息,然后利用提出的残差空洞注意力模型捕捉底层语义信息,并遵循采样规则设置合适且有效的采样率,在保持特征图高分辨率的同时有效地聚合多尺度上下文信息。最后,本文还设计了一个融合模块将空间CNN和残差空洞注意力产生的结果进行有效地融合。本文的方法在CULane和CamVid两种交通数据集上进行了一系列仿真实验,取得了可竞争性的结果,证明了所提方法的有效性。
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面向交通场景理解的基于残差空洞注意力的语义分割方法
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