液晶与显示2024，Vol.39Issue(7)：980-989,10.DOI:10.37188/CJLCD.2023-0208
融合多维特征的街景图像语义分割方法
Semantic segmentation method for street images with multi-dimensional features
摘要
Abstract
To further enhance the segmentation accuracy of deep learning semantic segmentation method on complex street images,this paper proposes a semantic segmentation network(MDFNet)incorporating multi-dimensional features based on PointRend network of street image.Firstly,the algorithm builds a target area enhancement module to optimize the feature extraction sub-network,which self-adaptively refines the intermediate feature map in each convolutional block of the deep network.Thus,the module enhances the fine extraction of multi-dimensional feature information of complex street images.Secondly,the paper introduces feature pyramid grid during feature fusion.The module uses different convolutional kernels to process street images of different scales.Thus,it obtains more comprehensively the different resolution features of various targets in complex street images.Finally,we use the double decoder to recover the details of the image in more detail to obtain the pixel-by-pixel classification results.The experimental results show that the network in this paper has higher segmentation accuracy on the Cityscapes dataset compared with other excellent networks such as DeepLabV3 and SegFormer.The mean intersection over union reaches 80.11%and an improvement of more than 3.51%compared to other networks.The method provides better understanding of images of complex street scenes.关键词
语义分割/目标区域增强/注意力机制/特征金字塔网格/多维特征Key words
semantic segmentation/target area enhancement/attention mechanism/feature pyramid grid/multi-dimensional features分类
信息技术与安全科学引用本文复制引用
朱磊,车晨洁,姚同钰,潘杨,张博..融合多维特征的街景图像语义分割方法[J].液晶与显示,2024,39(7):980-989,10.基金项目
国家自然科学基金(No.61971339) （No.61971339）
陕西省重点研发计划(No.2019GY-113) （No.2019GY-113）
陕西省自然科学基础研究计划(No.2019JQ-361)Supported by National Natural Science Foundation of China(No.61971339) （No.2019JQ-361）
Key R&D Program of Shaanxi Province(No.2019GY-113) （No.2019GY-113）
Shaanxi Provincial Natural Science Basic Research Program(No.2019JQ-361) （No.2019JQ-361）
