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一个基于权重的动态波形注意力神经网络用于缺陷检测.

Jinhai Liu, He Zhao, Zhaolin Chen

    IEEE transactions on neural networks and learning systems
    |July 12, 2023
    PubMed
    概括
    此摘要是机器生成的。

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    这项研究介绍了一种基于动态权重的新型波束注意力神经网络 (DWWA-Net) 用于工业缺陷检测. 在DWWA-Net有效地识别弱缺陷和那些在杂的图像,显著提高准确性.

    科学领域:

    • 计算机视觉 计算机视觉
    • 人工智能的人工智能
    • 材料科学 材料科学 材料科学

    背景情况:

    • 工业缺陷检测对于生产质量至关重要.
    • 当前的深度学习方法与弱缺陷和强大的背景噪音作斗争.
    • 为了提高准确性,需要改进特征表示和降噪.

    研究的目的:

    • 提出一种新的深度学习模型,即基于动态权重的波束注意力神经网络 (DWWA-Net),用于增强工业缺陷检测.
    • 为了解决检测弱缺陷和在显著背景噪音下缺陷的局限性.
    • 提高自动缺陷检测系统的整体准确性和稳定性.

    主要方法:

    • 开发一个基于动态权重的波束注意力神经网络 (DWWA-Net).
    • 波纹神经网络和动态波纹卷积网络 (DWCNets) 的集成,用于噪声过和模型融合.
    • 实施多视图关注模块,以专注于潜在的缺陷目标.
    • 包括一个功能反模块来增强缺陷功能信息.

    主要成果:

    • 拟议的DWWA-Net与最先进的方法相比,显示出更高的性能.
    • 在基准数据集中观察到平均精度的显著改善 (GC10-DET:增加6.0%,NEU:增加4.3%).

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  • 该方法有效地增强了特征表示,并减少了背景噪声.

    • 结论:

    • 对于具有挑战性的工业缺陷检测场景,DWWA-Net提供了一个强大的解决方案.
    • 该模型处理微弱缺陷和噪音图像的能力标志着一个显著的进步.
    • 在各种工业检查领域,DWWA-Net显示出广泛应用的潜力.