郑州大学学报（理学版）2026，Vol.58Issue(5)：54-61,8.DOI:10.13705/j.issn.1671-6841.2025004
融合FCN和ECA的FE-CCNN图像语义分割算法
FE-CCNN Image Semantic Segmentation Algorithm Fusing FCN and ECA
摘要
Abstract
To address the issues of discontinuous segmentation,incorrect segmentation,and the difficulty of segmenting object edges in image semantic segmentation tasks,a FE-CCNN algorithm for image seman-tic segmentation that integrated fully convolutional network(FCN)and efficient channel attention(ECA)was proposed.Firstly,ResNet-50 was adopted as the backbone network of the FCN model to extract im-age features.Secondly,the cascaded convolutional neural network(CCNN)was introduced to enhance the capability of image feature extraction,followed by the application of the ECA attention mechanism to weight the importance of feature channels.Finally,the the feature maps were upsampled to the original image size to output the segmentation results.The proposed algorithm was tested on the PASCAL_VOC 2012 dataset and the CamVid dataset,achieving pixel accuracy(PA)of 93.5%and 89.7%,and mean intersection over union(mIoU)scores of 71.2%and 70.2%,respectively,which were superior to those of other comparative algorithms.Moreover,via ablation studies,the effectiveness of the FE-CCNN algo-rithm in image semantic segmentation tasks was further verified.关键词
图像语义分割/全卷积网络/连续耦合神经网络/高效注意力机制Key words
image semantic segmentation/fully convolutional network/continuous-coupled neural net-work/efficient channel attention分类
信息技术与安全科学引用本文复制引用
刘欢,张莉,安新磊,常举越..融合FCN和ECA的FE-CCNN图像语义分割算法[J].郑州大学学报（理学版）,2026,58(5):54-61,8.基金项目
甘肃省自然科学基金项目(23JRRA861,24JRRA291) （23JRRA861,24JRRA291）
兰州工业学院青年科技创新项目(2021KJ-08) （2021KJ-08）
