自动化工业复合纤维定向检查使用基于注意的规范化深层网络
Yuanye Xu1,2,3,4, Yinlong Zhang1,2,3,5, Wei Liang1,2,3,5
1Key Laboratory of Networked Control System, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.
Micromachines
|July 8, 2023
概括
这项研究使用基于注意力的深度Hough网络 (DHN) 增强了在纤维增强复合材料 (FRC) 中的纤维方向测量. 该方法通过解决FRC材料自动视觉检查中的异常来提高准确性.
科学领域:
- 材料科学 材料科学 材料科学
- 计算机视觉 计算机视觉
- 图像处理 图像处理
背景情况:
- 纤维增强复合材料 (FRC) 是关键材料,其性能取决于纤维的方向.
- 使用图像处理的自动视觉检查对于测量FRC中的纤维方向至关重要.
- 现有的方法,如深度Hough转换 (DHT) 与背景和长线异常作斗争.
研究的目的:
- 开发一种改进的方法,用于在FRC中准确测量纤维方向.
- 为了解决深度Hough转换 (DHT) 在处理现实世界的图像异常方面的局限性.
- 提高FRC材料自动视觉检查的可靠性.
主要方法:
- 引入深度霍夫正常化以减轻对线段异常的敏感性.
- 开发基于注意力的深度Hough网络 (DHN),以消除背景异常.
- 在模拟现实世界的异常场景的三个已建立的数据集上进行了广泛的评估.
主要成果:
- 提出的深度霍夫正常化有效检测到短,真正的"线状"结构.
- 基于注意力的DHN成功地消除了背景噪声,并识别了关键的光纤区域.
- 与最先进的方法相比,DHN在F测量,平均绝对误差 (MAE) 和根平均平方误差 (RMSE) 中取得了竞争性表现.
结论:
- 开发的基于注意力的深度Hough网络 (DHN) 为FRC的光纤方向测量提供了强大的解决方案.
- 该方法在处理工业应用中常见的图像异常方面取得了显著改进.
- 这一进步有助于更可靠的纤维增强复合材料的自动化质量控制.
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