文章摘要
郑斌军,孔玲君.基于DeepLabv3+的图像语义分割优化方法[J].包装工程,2022,43(1):187-194.
ZHENG Bin-jun,KONG Ling-jun.Image Semantic Segmentation Based on Enhanced DeepLabv3+ Network[J].Packaging Engineering,2022,43(1):187-194.
基于DeepLabv3+的图像语义分割优化方法
Image Semantic Segmentation Based on Enhanced DeepLabv3+ Network
投稿时间:2021-08-20  
DOI:10.19554/j.cnki.1001-3563.2022.01.024
中文关键词: 语义分割  注意力机制  深度可分离卷积  编码器-解码器
英文关键词: semantic segmentation  attention module  depthwise separable convolution  encoder-decoder
基金项目:一流专科高等职业教育专业建设项目(2020ylxm-1)
作者单位
郑斌军 上海理工大学上海 200093 
孔玲君 上海出版印刷高等专科学校上海 200093 
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中文摘要:
      目的 为了实现良好的图像语义分割精度,同时尽可能降低网络的参数量,加快网络训练速度,提出基于DeepLabv3+的图像语义分割优化方法。方法 编码器主干网络增加注意力机制模块,并采用更密集的特征池化模块有效聚合多尺度特征,同时使用深度可分离卷积降低网络计算复杂度。结果 基于CamVid数据集的对比实验显示,优化后网络的MIoU分数达到了71.03%,在像素精度、平均像素精度等其他方面的评价指标上较原网络有小幅提升,并且网络参数量降低了12%。在Cityscapes的测试数据集上的MIoU分数为75.1%。结论 实验结果表明,优化后的网络能够有效提取图像特征信息,提高语义分割精度,同时降低模型复杂度。文中网络使用城市道路场景数据集进行测试,可以为今后的无人驾驶技术的应用提供参考,具有一定的实际意义。
英文摘要:
      The work aims to propose an image semantic segmentation optimization method based on DeepLabv3+ network, so as to achieve good image semantic segmentation accuracy, reduce the amount of network parameters as much as possible and speed up network training. The backbone network of encoder was added with attention module and more intensive feature pooling module was used to effectively aggregate multi-scale features. The depthwise separable convolution was applied to reduce the computational complexity of the network. According to the comparison test based on CamVid data set, MIoU score of the enhanced network reached 71.03%, and pixel accuracy and other evaluation indexes such as average pixel accuracy slightly improved compared with the original network. Furthermore, parameters of network were reduced by 12%. The Miou score on the test data set of cityscapes was 75.1%. According to the experimental results, the improved network can effectively extract the feature information of image, improve the semantic segmentation accuracy, and reduce the complexity of the model. The proposed network is tested by the urban street scenes, which can provide reference for the future application of driverless technology, and has certain practical significance.
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