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arXiv · 2203.00436

Boundary Corrected Multi-scale Fusion Network for Real-time Semantic Segmentation

Tianjiao Jiang·Yi Jin·Tengfei Liang·Xu Wang·Yidong Li
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Abstract

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.

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Tianjiao Jiang, Yi Jin, Tengfei Liang, Xu Wang, Yidong Li. 2022-03-01. Boundary Corrected Multi-scale Fusion Network for Real-time Semantic Segmentation. https://doi.org/10.1109/icip46576.2022.9897907

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