边缘嵌入式多功能融合网络用于自动结账
Jicai Li1, Meng Zhu2, Honge Ren1,3
1College of Computer and Control Engineering, Northeast Forestry University, Harbin 150040, China.
Journal of imaging
|October 28, 2025
概括
这项研究引入了自动结账系统的新网络,通过图像改进购物清单的准确性. 边缘嵌入式多功能融合网络 (E2MF2Net) 增强了产品检测,特别是在阻塞的情况下.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人工智能的人工智能
背景情况:
- 自动结账 (ACO) 系统需要从图像中准确识别产品.
- 挑战包括产品封闭,众多类别和混乱的布局,要求强大的检测模型.
- 现有的模型在现实世界的结账场景中扎于概括和稳定性.
研究的目的:
- 开发一个新的网络,即边缘嵌入式多功能融合网络 (E2MF2Net),用于从结账图像中准确生成购物清单.
- 在ACO任务中增强检测模型的稳定性和泛化能力.
- 改进合成图像生成以获得更好的训练数据.
主要方法:
- 提出了边缘嵌入式多功能融合网络 (E2MF2Net),以共同优化合成图像生成和特征建模.
- 引入了层次化口罩导向组合 (HMGC) 用于具有遮蔽耐受性的光现实合成图像生成.
- 整合了边缘嵌入式增强模块 (E3) 和多功能融合模块 (MFF),以改进特征提取和融合.
主要成果:
- 在RPC数据集上,E2MF2Net实现了最先进的检查准确性 (cAcc):98.52% (简单),97.95% (中等),96.52% (难度) 和97.62% (平均).
- 在硬模式中表现出3.63个百分点的显著改善,这表明与封闭产品的性能优越.
- 在增量学习和域泛化场景中表现出强大的稳定性和适应性.
结论:
- E2MF2Net有效地解决了产品封闭和复杂布局在自动结账任务中的挑战.
- 拟议的网络显著提高了从结账图像生成购物清单的准确性和稳定性.
- 该方法在现实世界部署先进的自动结账系统方面表现有前途.
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