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Updated: Jan 29, 2026

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PSgANet:极性序列引导注意网络,用于与接触镜片边缘相关的缺陷分类
Sung-Hoon Kim1, In Joo1, Kwan-Hee Yoo1
1Department of Computer Science, Chungbuk National University, Cheongju 28644, Republic of Korea.
Sensors (Basel, Switzerland)
|January 28, 2026
概括
这项研究引入了一种新型的人工智能 (AI) 方法,用于检测隐形眼镜缺陷. 极地序列引导注意网络 (PSgANet) 显著提高了识别边缘连接区域 (RCZ) 缺陷的准确性.
科学领域:
- 工业流程优化优化 工业流程优化
- 人工智能在制造业中的应用
- 隐形镜头质量控制 隐形镜头质量控制
背景情况:
- 隐形眼镜的制造要求高精度和安全性.
- 现有的AI方法在边缘连接区域 (RCZ) 中遇到微妙的缺陷.
- 需要先进的人工智能来提高运营效率和质量保证.
研究的目的:
- 引入一种新的AI方法,用于准确检测隐形眼镜缺陷.
- 开发一个定制的深度学习模型,PSgANet,用于RCZ缺陷识别.
- 提高隐形眼镜制造中缺陷的准确性和分类.
主要方法:
- 采用极坐标转换来线性化边缘不规则.
- 开发了极地序列引导注意网络 (PSgANet),集成序列学习和注意机制.
- 将PSgANet (GRU,LSTM,变压器) 与传统模型 (GoogleNetv4,EfficientNet,Vision Transformer) 进行了比较.
主要成果:
- PSgANet模型的表现优于传统的基于卷积神经网络 (CNN) 的模型.
- 基于LSTM的PSgANet实现了最高的准确性,精度和回忆.
- 与谷歌网tv4.4相比,其准确度提高了7.75%.
结论:
- 拟议的PSgANet方法对于检测和分类RCZ缺陷是有效的.
- 人工智能集成,特别是PSgANet,提高了隐形眼镜生产的质量控制.
- 这种方法为医疗器械制造业严格的安全要求提供了有希望的解决方案.
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