吉林大学学报(理学版) ›› 2022, Vol. 60 ›› Issue (6): 1356-1362.

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基于改进SOLO网络的城市道路场景实例分割方法

徐博文, 卢奕南   

  1. 吉林大学 计算机科学与技术学院, 长春 130012
  • 收稿日期:2021-12-07 出版日期:2022-11-26 发布日期:2022-11-26
  • 通讯作者: 卢奕南 E-mail:luyn@jlu.edu.cn

Urban Road Scene Instance Segmentation Method Based on Improved SOLO Network

XU Bowen, LU Yinan   

  1. College of Computer Science and Technology, Jilin University, Changchun 130012, China
  • Received:2021-12-07 Online:2022-11-26 Published:2022-11-26

摘要: 针对传统实例分割方法在无人驾驶技术中处理多目标分割准确率较低的问题, 提出一种基于联合注意力机制的深度学习方法, 实现城市交通多目标场景的实例分割. 该方法通过设计联合注意力模块, 对通道注意力与空间注意力进行融合, 引导神经网络分支处理重要特征信息, 以提升网络对多尺度目标的分割性能, 解决了当前深度学习网络对城市交通多目标场景分割效果较差的问题. 在城景数据集上的实验结果表明, 该方法有效, 可提升无人驾驶技术在城市道路交通场景下对实例分割的精确度.

关键词: 实例分割, 注意力机制, 路景分割, 深度学习

Abstract: Aiming at the low accuracy of traditional instance segmentation methods in handling multi-object segmentation in unmanned driving technology, we proposed a deep learning method based on joint attention mechanism to achieve instance segmentation of urban traffic multi-object scenes. The method in tegrated channel attention and spatial attention by designing the joint attention module,  guided the neural network branches to process important feature information, so as to improve the performance of the network for multi-scale object segmentation, and solved the problem of poor segmentation effect of the current deep learning network for urban traffic multi-object scene. The  experimental results on cityscape dataset show that  the proposed method is effective and  can improve the accuracy of instance segmentation by unmanned driving technology in urban road traffic scenes.

Key words: instance segmentation, attention mechanism, road scene segmentation, deep learning

中图分类号: 

  • TP391.4