基于自适应多尺度特征融合的多任务语义分割网络

Huilin Chen, Shengsong Yang, Ting Lyu
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引用次数: 1

摘要

提出了一种基于自适应多尺度特征融合的多任务语义分割网络架构,将边界检测任务与语义分割任务相结合,提高了分割目标边缘细节和小尺度目标分割精度。该体系结构的关键组成部分是自适应多尺度特征融合模块,该模块可以自适应融合不同尺度的语义特征信息和边界特征信息,提取包含更多空间数据的语义特征,减少小尺度目标空间信息的丢失,从而增强网络学习小尺度目标特征和边界特征的能力。实验表明,所设计的网络结构可以提高小尺度目标的分割精度,并优化分割目标的边缘细节。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multitask Semantic Segmentation Network Using Adaptive Multiscale Feature Fusion
A multi-task semantic segmentation network architecture based on adaptive multi-scale feature fusion is proposed, which improves segmentation target edge details and small-scale target segmentation accuracy by combining boundary detection tasks and semantic segmentation tasks. The critical component of the architecture is the adaptive multi-scale feature fusion module, which can adaptively fuse the semantic feature information and boundary feature information of different scales, extract semantic features that contain more spatial data, and reduce the loss of spatial information of small-scale targets, thereby enhancing the network's ability to learn small-scale target features and boundary features. Experiments show that our designed network architecture can improve the segmentation accuracy of small-scale objects and optimize the edge details of segmented objects.
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