Although deep neural networks have made significant progress in semantic segmentation, speed and computational cost still can’t meet the strict requirements of real-world applications. In this paper, we present an enhanced asymmetric convolution network (EACNet) to seek a balance between accuracy and speed. Specifically, we design a pair of enhancing asymmetric convolution modules constructed by depth-wise asymmetric convolution and dilated convolution to extract short-range and long-range features, which are efficient and powerful. Additionally, we apply a bilateral structure in which the detail branch preserves low-level spatial details while the semantic branch captures high-level context information. The two branches are merged at different stages of the network to strengthen information propagation between different levels. The experiments on the Cityscapes dataset show that our method achieves high accuracy and speed with relatively small parameters. Compared with other real-time semantic segmentation methods, our network attains a good trade-off among parameters, speed, and accuracy.

中文翻译：

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EACNet：用于实时语义分割的增强型非对称卷积

尽管深度神经网络在语义分割方面取得了长足的进步，但是速度和计算成本仍然不能满足实际应用的严格要求。在本文中，我们提出了一种增强的非对称卷积网络（EACNet），以寻求准确性和速度之间的平衡。具体来说，我们设计了一对增强的非对称卷积模块，这些模块由深度非对称卷积和膨胀卷积构造而成，以提取有效且强大的短程和远程特征。此外，我们应用了一种双边结构，其中细节分支保留了低级空间细节，而语义分支则捕获了高级上下文信息。这两个分支在网络的不同阶段合并，以加强不同级别之间的信息传播。在Cityscapes数据集上进行的实验表明，我们的方法使用相对较小的参数即可达到较高的准确性和速度。与其他实时语义分割方法相比，我们的网络在参数，速度和准确性之间取得了很好的折衷。