基于机器视觉的PCB板电解电容极性自动定位
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1.东南大学生物科学与医学工程学院 南京 210096;2.明锐理想科技有限公司 深圳 518000

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TP391.41;TP20

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Automatic polarity detection of PCB electrolytic capacitor based on machine vision
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1. School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China; 2. Magic Ray Technology Co., Ltd, Shenzhen 518000, China

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    摘要:

    现有电解电容极性检测通常分为内圆检测和极性检测两步流程。现有基于GHT的检测方法在内圆亮度非常接近外部时,无法准确定位内圆;基于滑动窗口平滑度的极性检测方法没有考虑极性区域亮度低、噪声大以及非极性区域存在高亮污染这三种情况,应用范围有限。针对现有方法不足,本文首先提出了基于超像素聚类分割的内圆检测方法,同时考虑了图像超像素间的亮度和位置关系,实现了对电解电容内圆的精确定位,算法稳定性能好,精度高。在内圆检测基础上,本文提出基于滑动窗口灰度均值和标准差峰谷值的电容极性检测方法,可实现复杂情况下的极性定位。相对现有算法,本文算法不依赖人工标记极性方向,可实现全自动化检测。目前该算法的测试准确率为98.6%,单张图像的平均检测时间为192ms±23ms,且已投入工厂使用,效果良好。

    Abstract:

    The most popular electrolytic capacitor detection method can be divided into two stages:inner circle detection and polarity detection. However, when affected by the light, the luminance of the inner circle can be very close to that of the outside, the small difference is difficult to be captured by GHT-based circle detection, thus making the inner circle mislocated. In addition, the polarity detection method based on the sliding window smoothness does not consider the three cases of low luminance or high noise in the polar region and the existence of high luminance pollution in the non-polar region, so the application of this method is limited. In this paper, we propose a novel method of inner circle detection based on the clustering segmentation of super pixels, which takes the relationship of luminance and position among super pixels into consideration simultaneously and can obtain the correct position even under poor illumination conditions. Furthermore, we propose a polarity detection method based on the selection among peaks and valleys that calculated by gray-level mean and variance of the sliding windows. The method has the ability to handle the general cases as well as the three complex cases mentioned above. It is worth noting that with our approach it is possible to provide direction directly, which was not possible in any previous approach, and facilitate full automation. The experimental results show that the test accuracy of the method reaches 98.6%, and the average detection time of a single image is 192ms±23ms. The solution has been accepted by many manufacturers and performs well.

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魏嘉莉,王瑞丰,冀运景,罗守华.基于机器视觉的PCB板电解电容极性自动定位[J].电子测量技术,2021,44(16):148-155

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  • 在线发布日期: 2024-08-22
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