JIPS (한국정보처리학회)
Current Result Document :
| 한글제목(Korean Title) |
A Deep Learning-Based Image Semantic Segmentation Algorithm |
| 영문제목(English Title) |
A Deep Learning-Based Image Semantic Segmentation Algorithm |
| 저자(Author) |
Chaoqun Shen
Zhongliang Sun
|
| 원문수록처(Citation) |
VOL 19 NO. 01 PP. 0098 ~ 0108 (2023. 02) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
This paper is an attempt to design segmentation method based on fully convolutional networks (FCN) and attention mechanism. The first five layers of the Visual Geometry Group (VGG) 16 network serve as the coding part in the semantic segmentation network structure with the convolutional layer used to replace pooling to reduce loss of image feature extraction information. The up-sampling and deconvolution unit of the FCN is then used as the decoding part in the semantic segmentation network. In the deconvolution process, the skip structure is used to fuse different levels of information and the attention mechanism is incorporated to reduce accuracy loss. Finally, the segmentation results are obtained through pixel layer classification. The results show that our method outperforms the comparison methods in mean pixel accuracy (MPA) and mean intersection over union (MIOU). |
| 키워드(Keyword) |
Attention Mechanism
FCN
Image Semantic Segmentation
Skip Structure
VGG16
|
| 파일첨부 |
PDF 다운로드
|
