基于可分离金字塔的轻量级实时语义分割算法

针对现有语义分割算法参数量过多、内存占用巨大导致其很难满足自动驾驶需要等现实应用的问题，提出一种基于可分离金字塔模块（SPM）的新颖、有效且轻量的实时语义分割算法。首先，利用特征金字塔形式的分解卷积和扩张卷积来构建瓶颈结构，从而以一种简单但有效的方式提取局部和上下文信息；然后，提出基于计算机视觉注意力的上下文通道注意力（CCA）模块，来利用深层语义修改浅层特征图通道权重优化分割效果。实验结果显示：所提出的算法在Cityscapes测试集上以每秒91帧的速度达到了71.86%的平均交并比（mIoU）。相较高效残差分解卷积网络（ERFNet），所提算法mIoU提高了3.86个百分点，处理速度是其2.2倍；与最新的非局部高效实时算法（LRNNet）相比，所提算法mIoU略低0.34个百分点，但处理速度每秒上升了20帧。实验结果表明，所提算法有助于完成如自动驾驶中要求的高效、准确的街道场景图像分割任务。

Lightweight real-time semantic segmentation algorithm based on separable pyramid

The existing semantic segmentation algorithms have too many parameters and huge memory usage, so that it is difficult to meet the requirements real-world applications such as automatic driving. In order to solve the problem, a novel, effective and lightweight real-time semantic segmentation algorithm based on Separable Pyramid Module (SPM) was proposed. Firstly, factorized convolution and dilated convolution were adopted in the form of a feature pyramid to construct the bottleneck structure, providing a simple but effective way to extract local and contextual information. Then, the Context Channel Attention (CCA) module based on computer vision attention was proposed to modify the channel weights of shallow feature maps by utilizing deep semantic features, thereby optimizing the segmentation results. Experimental results show that without pre-training or any additional processing, the proposed algorithm achieves mean Intersection-over-Union (mIoU) of 71.86% on Cityscapes test set at the speed of 91 Frames Per Second (FPS). Compared to Efficient Residual Factorized ConvNet (ERFNet), the proposed algorithm has the mIoU 3.86 percentage points higher, and the processing speed of 2.2 times. Compared with the latest Light-weighted Network with Efficient Reduced Non-local operation for real-time semantic segmentation (LRNNet), the proposed algorithm has the mIoU slightly lower by 0.34 percentage points, but the processing speed increased by 20 FPS. The experimental results show that the proposed algorithm has great value for completing tasks such as efficient and accurate street scene image segmentation required in automatic driving.