论文标题
实时语义分割的边界更正的多尺度融合网络
Boundary Corrected Multi-scale Fusion Network for Real-time Semantic Segmentation
论文作者
论文摘要
图像语义分段旨在对图像的像素级分类，该分类对实际应用的准确性和速度都有要求。现有的语义细分方法主要依赖于高分辨率输入来实现高精度，并且不符合推理时间的要求。尽管某些方法着眼于使用轻量级体系结构来解析高速场景，但它们在低计算下的语义功能中无法完全挖掘出相对较低的性能。为了实现实时和高精度的分割，我们提出了一种名为“边界校正的多尺度融合网络”的新方法，该方法使用设计的低分辨率多尺度融合模块来提取语义信息。此外，要处理由低分辨率特征图融合引起的边界误差，我们进一步设计了一个额外的边界校正损失，以约束过度平滑的特征。广泛的实验表明，我们的方法实现了实时语义分割的准确性和速度的最新平衡。
Image semantic segmentation aims at the pixel-level classification of images, which has requirements for both accuracy and speed in practical application. Existing semantic segmentation methods mainly rely on the high-resolution input to achieve high accuracy and do not meet the requirements of inference time. Although some methods focus on high-speed scene parsing with lightweight architectures, they can not fully mine semantic features under low computation with relatively low performance. To realize the real-time and high-precision segmentation, we propose a new method named Boundary Corrected Multi-scale Fusion Network, which uses the designed Low-resolution Multi-scale Fusion Module to extract semantic information. Moreover, to deal with boundary errors caused by low-resolution feature map fusion, we further design an additional Boundary Corrected Loss to constrain overly smooth features. Extensive experiments show that our method achieves a state-of-the-art balance of accuracy and speed for the real-time semantic segmentation.
