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WD-YOLO:一个更准确的YOLO用于接X射线图像中的缺陷检测

Kailai Pan¹, Haiyang Hu¹, Pan Gu¹

¹School of Computer Science, Hangzhou Dianzi University, Hangzhou 310018, China.

Sensors (Basel, Switzerland)

|November 14, 2023

概括

这项研究介绍了WD-YOLO,这是一种用于检测X射线图像中的接缺陷的新型模型. 它增强了对比度,并使用高级功能来准确有效地识别接缺陷.

科学领域:

工业非破坏性测试工业非破坏性测试
计算机视觉计算机视觉计算机视觉
人工智能的人工智能是人工智能.

背景情况:

在工业非破坏性测试中,X射线成像是至关重要的.
在接接的X射线图像中,低对比度和可变的缺陷特征阻碍了准确的缺陷检测.
现有的方法难以应对接缺陷识别的复杂性.

研究的目的:

开发一种有效的模型,用于检测工业接的X射线图像中的缺陷.
为了提高自动接缺陷检测的准确性和效率.
为了应对低对比度和各种缺陷外观所带来的挑战.

主要方法:

提出了一个灰色值曲线增强 (GCE) 模块来改善图像对比度.
开发了一种专门的接缺陷检测模型,名为WD-YOLO.
在WD-YOLO中利用特征金字塔和路径聚合网络.
引入了一个NeXt骨干,用于增强特征提取和融合.
在YOLO头部整合了双重注意力机制,以改善前景和背景的区别.

主要成果:

该GCE模块有效地提高了图像对比度,以更轻松地检测缺陷.
WD-YOLO在检测性能和速度之间取得了良好的平衡.
在0.5.5的交叉点对欧盟 (IoU) 值时,实现了92.6%的平均平均精度 (mAP).
以每秒98的速度处理图像,表明高效率.

结论:

拟议的WD-YOLO模型与GCE模块相结合,显著改善了X射线图像中的接缺陷检测.
该模型的架构,包括NeXt骨干和双重注意力机制,有助于其高精度和效率.
WD-YOLO为工业非破坏性测试应用中的自动化,可靠的检查提供了一个有前途的解决方案.

关键词:

这是一个YOLO YOLO.注意力机制注意力机制接缺陷检测检测检测检测

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