Improving the efficiency of state-of-the-art methods in semantic segmentation requires overcoming the increasing computational cost as well as issues such as fusing semantic information from global and local contexts. Based on the recent success and problems that convolutional neural networks (CNNs) encounter in semantic segmentation, this research proposes an encoder-decoder architecture with a unique efficient residual network, Efficient-ResNet. Attention-boosting gates (AbGs) and attention-boosting modules (AbMs) are deployed by aiming to fuse the equivariant and feature-based semantic information with the equivalent sizes of the output of global context of the efficient residual network in the encoder. Respectively, the decoder network is developed with the additional attention-fusion networks (AfNs) inspired by AbM. AfNs are designed to improve the efficiency in the one-to-one conversion of the semantic information by deploying additional convolution layers in the decoder part. Our network is tested on the challenging CamVid and Cityscapes datasets, and the proposed methods reveal significant improvements on the residual networks. To the best of our knowledge, the developed network, SERNet-Former, achieves state-of-the-art results (84.62 % mean IoU) on CamVid dataset and challenging results (87.35 % mean IoU) on Cityscapes validation dataset.

翻译：提升语义分割最先进方法的效率需克服计算成本增加以及全局与局部语义信息融合等问题。针对卷积神经网络在语义分割中面临的最新进展与挑战，本研究提出一种采用独特高效残差网络Efficient-ResNet的编码器-解码器架构。通过在编码器中部署注意力增强门控（AbGs）与注意力增强模块（AbMs），旨在融合等变特征和基于特征的语义信息，使其与高效残差网络全局上下文输出尺寸保持等效。解码器网络则基于AbM启发，额外构建注意力融合网络（AfNs）。AfNs通过在解码器部分增设卷积层，旨在提升语义信息一对一转换的效率。本网络在具有挑战性的CamVid和Cityscapes数据集上进行了测试，所提方法在残差网络上展现出显著改进。据我们所知，所开发的SERNet-Former网络在CamVid数据集上取得了最先进的结果（平均交并比84.62%），并在Cityscapes验证数据集上取得了具有挑战性的结果（平均交并比87.35%）。