In this paper, we present a new network named Attention Aware Network (AASeg) for real time semantic image segmentation. Our network incorporates spatial and channel information using Spatial Attention (SA) and Channel Attention (CA) modules respectively. It also uses dense local multi-scale context information using Multi Scale Context (MSC) module. The feature maps are concatenated individually to produce the final segmentation map. We demonstrate the effectiveness of our method using a comprehensive analysis, quantitative experimental results and ablation study using Cityscapes, ADE20K and Camvid datasets. Our network performs better than most previous architectures with a 74.4\% Mean IOU on Cityscapes test dataset while running at 202.7 FPS.

翻译：在本文中,我们展示了一个新的网络,名为 " 注意意识网络(AASeg) ",用于实时的语义图像分割。我们的网络包含分别使用空间注意(SA)和频道注意(CA)模块的空间和频道信息。它也使用多尺度环境(MSC)模块的密集多尺度本地背景信息。地貌地图是单独拼凑的,以制作最后的分区地图。我们使用城市景、ADE20K和Camvid数据集的全面分析、量化实验结果和反差研究,展示了我们方法的有效性。我们的网络比大多数以前的结构表现更好,在202.7FPS运行时,在城市景测试数据集上带有74.4 ⁇ 平均值IOUU。