基于YOLOv5-DCN-LSKK的高效打印缺陷检测
Jie Liu1, Zelong Cai1, Kuanfang He1
1School of Mechatronics Engineering and Automation, Foshan University, Foshan 528225, China.
Sensors (Basel, Switzerland)
|December 17, 2024
概括
这项研究引入了一种改进的YOLOv5模型,用于检测微妙的喷墨打印缺陷,提高准确性和速度. 改进后的模型显著提高了识别打印缺陷的性能,确保了产品信息的可读性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 制造业质量控制 制造业质量控制
背景情况:
- 喷墨打印标签容易出现影响产品信息可读性的缺陷.
- 现有的深度学习缺陷检测系统在与这些印刷缺陷的微妙和多样化的形状作斗争,限制了准确性和速度.
研究的目的:
- 开发一个改进的深度学习模型,用于高精度和快速检测喷墨标签中的打印缺陷.
- 为了增强对狭窄,延长和小印刷缺陷的检测能力.
主要方法:
- 提出了改进的YOLOv5网络架构,结合了C3-DCN模块,以加强对延长缺陷的检测.
- 大选择性内核 (LSK) 和RepConv模块被集成到功能融合网络中.
- 使用规范高斯瓦瑟斯坦距离 (NWD) 和高效IoU (EIoU) 的组合损失函数被用于改善小目标检测.
- 应用了模型修剪技术来减少模型大小并增加检测速度.
主要成果:
- 改进的YOLOv5模型实现了0.741.5的平均平均精度 (mAP@0.5).
- 改进后的模型显示检测速度为每秒323.2 (FPS).
- 性能指标显示,与原来的YOLOv5.5相比,mAP增加了2.7%,FPS增加了20.8%.
结论:
- 提议的增强型YOLOv5模型有效地解决了检测微妙和多样化的打印缺陷的挑战.
- 该方法符合工业印刷缺陷检测中高精度和高效率的严格要求.
- 这一进步有助于提高喷墨标签生产的质量控制.
更多相关视频
04:32Author Spotlight: Quantitative Characterization of Liquid Photosensitive Bioink Properties for Continuous Digital Light Processing Based Printing
Published on: April 14, 2023
824
10:57Elaborate Control of Inkjet Printer for Fabrication of Chip-based Supercapacitors
Published on: November 30, 2021
2.7K
相关概念视频
Detection of Gross Error: The Q Test
5.6K
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...
5.6K
Types of Errors: Detection and Minimization
1.4K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
1.4K
Reducing Line Loss
144
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
144
Improving Translational Accuracy
8.8K
Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
8.8K
Difference from Background: Limit of Detection
5.8K
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.
The LOD indicates the presence or absence...
The LOD indicates the presence or absence...
5.8K
Calibration Curves: Linear Least Squares
1.2K
A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
For data that follow a straight line, the standard method for fitting is the linear...
1.2K
