计算机科学 ›› 2020, Vol. 47 ›› Issue (6A): 161-165.doi: 10.11896/JsJkx.191200127
任天赐1, 黄向生2, 丁伟利1, 安重阳1, 翟鹏博3
REN Tian-ci1, HUANG Xiang-sheng2, DING Wei-li1, AN Chong-yang1 and ZHAI Peng-bo3
摘要： 语义分割任务是对图像中的物体按照类别进行像素级别的预测,其难点在于在保留足够空间信息的同时获取足够的上下文信息。为解决这一问题,文中提出了全局双边网络语义分割算法。该算法将大尺度卷积核融入BiSeNet网络中,在BiSeNet网络原有的空间路径和上下文路径两条分支的基础上增加全局路径分支,使网络能够捕获更多的上下文信息,同时提出将BiSeNet网络中的注意力优化模块和特征融合模块中的全局池化模块替换为全局卷积模块,进一步提高了网络获取上下文信息的能力,从而使预测结果更加准确。实验结果表明,该算法在Cityscapes数据集上将交并比(MIoU)指标提高了0.84%,获得了优于BiSeNet网络的表现。
中图分类号:
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