Packaging Design Image Segmentation Based on Improved Full Convolutional Networks
Packaging design plays a critical role in brand recognition and cultural dissemination, yet the traditional design process is time-consuming and dependent on the designer’s technical skills, making it difficult to quickly respond to market changes and consumer demands. In recent years, advancements...
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MDPI AG
2024-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/22/10742 |
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| author | Chunxiao Zhang Mengmeng Han Jingjing Jia Chulsoo Kim |
| author_facet | Chunxiao Zhang Mengmeng Han Jingjing Jia Chulsoo Kim |
| author_sort | Chunxiao Zhang |
| collection | DOAJ |
| description | Packaging design plays a critical role in brand recognition and cultural dissemination, yet the traditional design process is time-consuming and dependent on the designer’s technical skills, making it difficult to quickly respond to market changes and consumer demands. In recent years, advancements in machine learning, particularly in the field of natural language processing (NLP), have paved the way for novel methods in other areas, such as image processing and packaging design. This study draws inspiration from advanced NLP techniques and proposes an improved fully convolutional network (FCN) model for image semantic segmentation, which is applied to packaging design. The model integrates superpixel technology, multi-branch networks, dual-attention mechanisms, and edge knowledge distillation in a manner analogous to the approach taken by NLP models in the context of semantic segmentation and context understanding. The experimental results showed that the model achieved significant improvements in accuracy, inference efficiency, and memory usage, with an average accuracy of 96.84% and a false-alarm rate of only 2.78%. Compared to traditional methods, the proposed model achieved over 96% accuracy across 50 packaging design images, with an average segmentation error rate of only 1.42%. By incorporating machine learning techniques from NLP into image processing, this study enhances the overall quality and efficiency of packaging design and provides new directions for the application of advanced technologies across different fields. |
| format | Article |
| id | doaj-art-3dff03a0a2744d1bad5cc2c4605a566b |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-3dff03a0a2744d1bad5cc2c4605a566b2024-11-26T17:49:59ZengMDPI AGApplied Sciences2076-34172024-11-0114221074210.3390/app142210742Packaging Design Image Segmentation Based on Improved Full Convolutional NetworksChunxiao Zhang0Mengmeng Han1Jingjing Jia2Chulsoo Kim3College of Design and Fine Arts, Qingdao Huanghai University, 1145, Linghai Road, West Coast New District, Qingdao 266427, ChinaDepartment of Industrial Design, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Republic of KoreaDepartment of Industrial Design, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Republic of KoreaDepartment of Industrial Design, Pukyong National University, 45, Yongso-ro, Nam-Gu, Busan 48513, Republic of KoreaPackaging design plays a critical role in brand recognition and cultural dissemination, yet the traditional design process is time-consuming and dependent on the designer’s technical skills, making it difficult to quickly respond to market changes and consumer demands. In recent years, advancements in machine learning, particularly in the field of natural language processing (NLP), have paved the way for novel methods in other areas, such as image processing and packaging design. This study draws inspiration from advanced NLP techniques and proposes an improved fully convolutional network (FCN) model for image semantic segmentation, which is applied to packaging design. The model integrates superpixel technology, multi-branch networks, dual-attention mechanisms, and edge knowledge distillation in a manner analogous to the approach taken by NLP models in the context of semantic segmentation and context understanding. The experimental results showed that the model achieved significant improvements in accuracy, inference efficiency, and memory usage, with an average accuracy of 96.84% and a false-alarm rate of only 2.78%. Compared to traditional methods, the proposed model achieved over 96% accuracy across 50 packaging design images, with an average segmentation error rate of only 1.42%. By incorporating machine learning techniques from NLP into image processing, this study enhances the overall quality and efficiency of packaging design and provides new directions for the application of advanced technologies across different fields.https://www.mdpi.com/2076-3417/14/22/10742image semantic segmentationfully convolutional networksuperpixel technologyknowledge distillationpacking design |
| spellingShingle | Chunxiao Zhang Mengmeng Han Jingjing Jia Chulsoo Kim Packaging Design Image Segmentation Based on Improved Full Convolutional Networks Applied Sciences image semantic segmentation fully convolutional network superpixel technology knowledge distillation packing design |
| title | Packaging Design Image Segmentation Based on Improved Full Convolutional Networks |
| title_full | Packaging Design Image Segmentation Based on Improved Full Convolutional Networks |
| title_fullStr | Packaging Design Image Segmentation Based on Improved Full Convolutional Networks |
| title_full_unstemmed | Packaging Design Image Segmentation Based on Improved Full Convolutional Networks |
| title_short | Packaging Design Image Segmentation Based on Improved Full Convolutional Networks |
| title_sort | packaging design image segmentation based on improved full convolutional networks |
| topic | image semantic segmentation fully convolutional network superpixel technology knowledge distillation packing design |
| url | https://www.mdpi.com/2076-3417/14/22/10742 |
| work_keys_str_mv | AT chunxiaozhang packagingdesignimagesegmentationbasedonimprovedfullconvolutionalnetworks AT mengmenghan packagingdesignimagesegmentationbasedonimprovedfullconvolutionalnetworks AT jingjingjia packagingdesignimagesegmentationbasedonimprovedfullconvolutionalnetworks AT chulsookim packagingdesignimagesegmentationbasedonimprovedfullconvolutionalnetworks |