Deep Learning-Based Layout Analysis Method for Complex Layout Image Elements
The layout analysis of elements is indispensable in graphic design, as effective layout design not only facilitates the delivery of visual information but also enhances the overall esthetic appeal to the audience. The combination of deep learning and graphic design has gradually turned into a popula...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-07-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/14/7797 |
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| author | Yunfei Zhong Yumei Pu Xiaoxuan Li Wenxuan Zhou Hongjian He Yuyang Chen Lang Zhong Danfei Liu |
| author_facet | Yunfei Zhong Yumei Pu Xiaoxuan Li Wenxuan Zhou Hongjian He Yuyang Chen Lang Zhong Danfei Liu |
| author_sort | Yunfei Zhong |
| collection | DOAJ |
| description | The layout analysis of elements is indispensable in graphic design, as effective layout design not only facilitates the delivery of visual information but also enhances the overall esthetic appeal to the audience. The combination of deep learning and graphic design has gradually turned into a popular research direction in graphic design in recent years. However, in the era of rapid development of artificial intelligence, the analysis of layout still requires manual participation. To address this problem, this paper proposes a method for analyzing the layout of complex layout image elements based on the improved DeepLabv3++ model. The method reduces the number of model parameters and training time by replacing the backbone network. To improve the effect of multi-scale semantic feature extraction, the null rate of ASPP is fine-tuned, and the model is trained by self-constructed movie poster dataset. The experimental results show that the improved DeepLabv3+ model achieves a better segmentation effect on the self-constructed poster dataset, with MIoU reaching 75.60%. Compared with the classical models such as FCN, PSPNet, and DeepLabv3, the improved model in this paper effectively reduces the number of model parameters and training time while also ensuring the accuracy of the model. |
| format | Article |
| id | doaj-art-751dcc3b6c4f46ea921e5e6d3b7aa743 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-751dcc3b6c4f46ea921e5e6d3b7aa7432025-08-20T03:58:25ZengMDPI AGApplied Sciences2076-34172025-07-011514779710.3390/app15147797Deep Learning-Based Layout Analysis Method for Complex Layout Image ElementsYunfei Zhong0Yumei Pu1Xiaoxuan Li2Wenxuan Zhou3Hongjian He4Yuyang Chen5Lang Zhong6Danfei Liu7School of Packaging Engineering, Hunan University of Technology, Zhuzhou 412007, ChinaSchool of Packaging Engineering, Hunan University of Technology, Zhuzhou 412007, ChinaSchool of Packaging Engineering, Hunan University of Technology, Zhuzhou 412007, ChinaSchool of Packaging Engineering, Hunan University of Technology, Zhuzhou 412007, ChinaSchool of Packaging Engineering, Hunan University of Technology, Zhuzhou 412007, ChinaSchool of Packaging Engineering, Hunan University of Technology, Zhuzhou 412007, ChinaSchool of Packaging Engineering, Hunan University of Technology, Zhuzhou 412007, ChinaSchool of Packaging Engineering, Hunan University of Technology, Zhuzhou 412007, ChinaThe layout analysis of elements is indispensable in graphic design, as effective layout design not only facilitates the delivery of visual information but also enhances the overall esthetic appeal to the audience. The combination of deep learning and graphic design has gradually turned into a popular research direction in graphic design in recent years. However, in the era of rapid development of artificial intelligence, the analysis of layout still requires manual participation. To address this problem, this paper proposes a method for analyzing the layout of complex layout image elements based on the improved DeepLabv3++ model. The method reduces the number of model parameters and training time by replacing the backbone network. To improve the effect of multi-scale semantic feature extraction, the null rate of ASPP is fine-tuned, and the model is trained by self-constructed movie poster dataset. The experimental results show that the improved DeepLabv3+ model achieves a better segmentation effect on the self-constructed poster dataset, with MIoU reaching 75.60%. Compared with the classical models such as FCN, PSPNet, and DeepLabv3, the improved model in this paper effectively reduces the number of model parameters and training time while also ensuring the accuracy of the model.https://www.mdpi.com/2076-3417/15/14/7797DeepLabv3+image segmentationposter layout analysisdeep learning |
| spellingShingle | Yunfei Zhong Yumei Pu Xiaoxuan Li Wenxuan Zhou Hongjian He Yuyang Chen Lang Zhong Danfei Liu Deep Learning-Based Layout Analysis Method for Complex Layout Image Elements Applied Sciences DeepLabv3+ image segmentation poster layout analysis deep learning |
| title | Deep Learning-Based Layout Analysis Method for Complex Layout Image Elements |
| title_full | Deep Learning-Based Layout Analysis Method for Complex Layout Image Elements |
| title_fullStr | Deep Learning-Based Layout Analysis Method for Complex Layout Image Elements |
| title_full_unstemmed | Deep Learning-Based Layout Analysis Method for Complex Layout Image Elements |
| title_short | Deep Learning-Based Layout Analysis Method for Complex Layout Image Elements |
| title_sort | deep learning based layout analysis method for complex layout image elements |
| topic | DeepLabv3+ image segmentation poster layout analysis deep learning |
| url | https://www.mdpi.com/2076-3417/15/14/7797 |
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