基于视觉语义细分技术和智能网络物理系统的包装风格设计.
1College of Art and Design, Xi'an Mingde Institute of Technology, Xi'an, China.
PeerJ. Computer science
|August 7, 2023
概括
本研究介绍了G-Lite-DeepLabV3+,这是一个用于产品包装设计的新型图像细分网络. 它提高了网络物理系统 (CPS) 的准确性和效率,提高了包装的美学和实用性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 包装设计 包装设计
背景情况:
- 传统的图像细分算法耗时,可能会丢失关键功能,导致产品包装设计中的最佳结果不足.
- 提高包装设计的审美和实用方面需要准确和高效的图像细分.
- 网络物理系统 (CPS) 提供了一个集成先进图像处理技术的平台.
研究的目的:
- 引入一个新的细分网络,G-Lite-DeepLabV3 +,用于在CPS中改进产品包装图像细分.
- 为了提高包装设计应用的图像细分的准确性和效率.
- 为了解决传统细分方法在捕获复杂细节和处理速度方面的局限性.
主要方法:
- 通过将DeepLabV3的特征提取网络替换为Mobilenetv2.2,开发了G-Lite-DeepLabV3+
- 集成群体卷积和注意力机制,以处理复杂的语义特征并提高响应能力.
- 在网络物理系统 (CPS) 中部署了G-Lite-DeepLabV3+网络,用于远程实时图像分割.
主要成果:
- G-Lite-DeepLabV3+在产品包装图像中展示了各种图形元素的优越细分.
- 与DeepLabV3+相比,实现了交叉与联盟 (IoU) 的增长3.1%,平均像素精度 (mPA) 的提高6.2%.
- 显著提高了处理速度22.1% (每秒 - FPS),表明提高了效率.
结论:
- G-Lite-DeepLabV3+网络有效地提高了产品包装图像细分的准确性和效率.
- 整合Mobilenetv2,群组卷积和注意力机制可以提高功能处理和网络性能.
- 这种新的方法促进了实时,准确的细分,有助于先进的包装设计和虚拟环境应用.
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