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图学学报

图学学报 ›› 2021, Vol. 42 ›› Issue (3): 406-413.DOI: 10.11996/JG.j.2095-302X.2021030406

• 图像处理与计算机视觉 • 上一篇    下一篇

结合超像素分割的多尺度特征融合图像语义分割算法 

  

  1. 1. 上海师范大学信息与机电工程学院，上海 200234；  2. 上海交通大学电子信息与电气工程学院，上海 200240
  • 出版日期:2021-06-30 发布日期:2021-06-29
  • 基金资助:
    国家自然科学基金项目(61872242) 

A semantic segmentation algorithm using multi-scale feature fusion with combination of superpixel segmentation 

  1. 1. College of Information, Mechanical and Electrical Engineering, Shanghai Normal University, Shanghai 200234, China;  2. College of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
  • Online:2021-06-30 Published:2021-06-29
  • Supported by:
    National Natural Science Foundation of China (61872242) 

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摘要： 深度学习的发展加快了图像语义分割的研究。目前，最有效的图像语义分割研究方法大部分都是 基于全卷积神经网络(FCNN)，尽管现有的语义分割方法能有效地对图像进行整体分割，但对于图像中的重叠遮 挡物体不能清晰地识别出边缘信息，也不能有效地融合图像高低层的特征信息。针对以上问题，在采用 FCNN 来 解决图像语义分割问题的基础上，利用超像素分割对物体边缘的特殊优势作为辅助优化，对粗糙分割结果进行优 化。同时在 FCNN 中利用空洞卷积设计了一个联合局部跨阶段的多尺度特征融合模块，其能有效地利用图像的空 间信息。此外还在网络的上采样模块中加入跳跃连接结构，用来增强网络的学习能力，在训练过程中采用 2 个损 失函数来保证网络稳定收敛和提升网络的性能，图像语义分割网络在公开的数据集 PASCAL VOC 2012 上进行训 练测试。实验结果表明，该改进算法在像素精度和分割准确率方面均有提升，且具有较强的鲁棒性。

关键词: 全卷积神经网络, 多尺度特征融合, 超像素分割

Abstract: The advancement of deep learning has boosted the research on image semantic segmentation. At present, most effective methods for this research are based on the fully convolutional neural networks. Although the existing semantic segmentation methods can effectively segment the image as a whole, they cannot clearly identify the edge information of the overlapped objects in the image, and cannot effectively fuse the high- and low-layer feature information of the image. To address the above problems, superpixel segmentation was employed as an auxiliary optimization to optimize the segmentation results of object edges based on the fully convolutional neural network. At the same time, the design of a joint cross-stage partial multiscale feature fusion module can enable the utilization of image spatial information. In addition, a skip structure was added to the upsampling module to enhance the learning ability of the network, and two loss functions were adopted to ensure network convergence and improve network performance. The network was trained and tested on the public datasets PASCAL VOC 2012. Compared with other image semantic segmentation methods, the proposed network can improve the accuracies in pixel and segmentation, and displays strong robustness. 

Key words:  fully convolutional neural network, multiscale feature fusion, superpixel segmentation

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