FBSNet：一种用于实时的语义分割的快速双边对称网络
FBSNet: A Fast Bilateral Symmetrical Network for Real-Time Semantic Segmentation
摘要：
实时的【形】语义分割，可以直观地理解为对输入图像进行像素级的分类任务，目前具有广阔的应用前景，尤其是在自动驾驶和无人机导航等快速发展的领域。然而，其庞大的计算负担和多余的参数仍然是其技术发展的障碍。在本文中，我们提出了一个快速双边对称网络（FBSNet），以减轻上述挑战。具体来说，FBSNet采用了对称的编码器-解码器结构，具有两个分支，语义信息分支和空间细节分支。语义信息分支（SIB）是语义架构的主要分支，用于获取输入图像的上下文信息，同时获得足够的感受野。而空间细节分支（SDB）是一个浅而简单的网络，用于建立每个像素的局部依赖关系以保留细节，这对于在解码阶段恢复原始分辨率至关重要。同时，设计了一个特征聚合模块（FAM），有效地结合这两个分支的输出。Cityscapes和CamVid的实验结果表明，FBSNet能够在准确性和效率之间取得较好的平衡。具体来说，它在这两个测试数据集上分别获得了70.9%和68.9%的mIoU以及90 fps和120 fps的推理速度，在单个RTX 2080 Ti GPU上只有62万个参数。该代码可在https://github.com/IVIPLab/FBSNet上获得。
英文摘要：
Real-time semantic segmentation, which can be visually understood as the pixel-level classification task on the input image, currently has broad application prospects, especially in the fast-developing fields of autonomous driving and drone navigation. However, the huge burden of calculation together with redundant parameters are still the obstacles to its technological development. In this paper, we propose a Fast Bilateral Symmetrical Network (FBSNet) to alleviate the above challenges. Specifically, FBSNet employs a symmetrical encoder-decoder structure with two branches, semantic information branch and spatial detail branch. The Semantic Information Branch (SIB) is the main branch with semantic architecture to acquire the contextual information of the input image and meanwhile acquire sufficient receptive field. While the Spatial Detail Branch (SDB) is a shallow and simple network used to establish local dependencies of each pixel for preserving details, which is essential for restoring the original resolution during the decoding phase. Meanwhile, a Feature Aggregation Module (FAM) is designed to effectively combine the output of these two branches. Experimental results of Cityscapes and CamVid show that the proposed FBSNet can strike a good balance between accuracy and efficiency. Specifically, it obtains 70.9\% and 68.9\% mIoU along with the inference speed of 90 fps and 120 fps on these two test datasets, respectively, with only 0.62 million parameters on a single RTX 2080Ti GPU. The code is available at https://github.com/IVIPLab/FBSNet.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
