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相关概念视频

Detection of Gross Error: The Q Test01:00

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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Electronic Distance Measuring Instruments (EDMs) are essential tools in modern surveying, offering precise distance measurements by emitting electromagnetic signals and calculating the time required for these signals to travel to a target and return. Two primary types of signals are used in EDMs — light waves and microwaves — each suited to specific environmental and distance requirements. Light-wave-based EDMs utilize either infrared or laser light, providing high accuracy over...
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The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
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IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the...
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Subsurface Defect Localization by Structured Heating Using Laser Projected Photothermal Thermography
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基于改进的YOLOv11的电子产品表面缺陷检测方法.

Jianming Meng1, Longjian Guo1, Wei Hao1

  • 1Department of Electronic and Communication Engineering, Shandong College of Electronic Technology, Jinan, China.

PloS one
|October 28, 2025
PubMed
概括

本研究引入了一种改进的YOLOv11模型,用于在电子产品中增强表面缺陷检测. 新方法显著提高了精度和召回率,为制造质量控制提供了更可靠的自动化解决方案.

科学领域:

  • 计算机视觉 计算机视觉
  • 机器学习 机器学习
  • 制造业 制造技术 制造技术

背景情况:

  • 电子产品的传统手动检查方法受到操作员的变化性限制,并与对效率和精度的日益增长的要求作斗争.
  • 深度学习,特别是对象检测,在制造业自动化质量控制方面显示出前景.
  • 现有的YOLO模型在复杂的背景下检测小缺陷方面面临挑战.

研究的目的:

  • 开发一个改进的基于YOLOv11的深度学习模型,用于准确检测电子产品的表面缺陷.
  • 解决当前YOLO模型在识别小缺陷和复杂背景方面的局限性.
  • 为了提高自动缺陷检测系统的精度,回忆率和检测速度.

主要方法:

  • 提出了改进的YOLOv11架构,其中包括MD-C2F模块,DualConv模块和Inner_MPDIoU损失函数.
  • 该模型经过训练和评估,用于电子产品的表面缺陷检测.
  • 性能与之前的YOLO版本 (YOLOv7,YOLOv8,YOLOv9) 以及PKU-Market-PCB数据集进行了比较.

主要成果:

  • 改进的YOLOv11模型实现了93.1%的精度 (高于90.9%) 和84.6%的召回率 (高于77.0%).
  • mAP50增长了4.6%至88.6%,在检测电阻,LED灯和电容器等各种缺陷方面超过了其他YOLO版本.

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  • 在PKU-Market-PCB数据集上的泛化测试显示了准确度 (94.6%),回忆 (91.2%) 和mAP50 (95.4%) 的提高.

    • 结论:

    • 拟议的YOLOv11模型有效地克服了在复杂的背景和不同尺度中检测小缺陷的挑战.
    • 检测准确度,回忆和概括能力显著提高.
    • 改进的YOLOv11为电子产品制造中的缺陷检测提供了强大的自动化解决方案.