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

• URL: http://arxiv.org/abs/2203.00436v1
• Date: Tue, 1 Mar 2022 13:31:01 GMT
• Title: Boundary Corrected Multi-scale Fusion Network for Real-time Semantic Segmentation
• Authors: Tianjiao Jiang, Yi Jin, Tengfei Liang, Xu Wang, Yidong Li
• Abstract summary: Existing semantic segmentation methods rely on the high-resolution input to achieve high accuracy and do not meet the requirements of inference time. 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. Our method achieves a state-of-the-art balance of accuracy and speed for the real-time semantic segmentation.
• Score: 15.879949436633021
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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