Research on the Recognition Algorithm of Circuit Board Welding Defects Based on Machine Vision

IF 1.6 4区工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC

IEEE Instrumentation & Measurement Magazine Pub Date : 2023-11-01 DOI:10.1109/mim.2023.10292622

Rui Wang, Peng Wang, Nan Chen, Yaoyuan Wang

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引用次数: 0

Abstract

To improve the defect detection ability of circuit boards and reduce the missed detection rate and false detection rate, a circuit board welding defect recognition algorithm based on machine vision is proposed. The system obtains the grayscale image of the circuit board to be tested through X-ray source, image intensifier and a Charge Coupled Device (CCD). Noise suppression is performed on all test images using a cumulative sampling noise reduction algorithm. The defect recognition algorithm is realized by using a standard template matching model with multi-angle image acquisition. By setting the best template matching parameter (BTM), the difference area extraction between the test image and the standard image is completed. Then, the calibration transformation of different perspectives is used to complete the iteration of the feature information of the defect area, and the ability of defect detection and identification is improved. The experiment is tested on 15 circuit board images with different types of defects. The results show that the missed detection rates of this algorithm for bridge defects, eccentric defects and solder joint bubble defects are 0.58%, 1.18%, 1.95%, and the false detection rates were 0.12%, 0.86%, 2.34%, respectively. It is significantly better than traditional algorithms. In terms of processing speed and maximum fitness, this algorithm is also slightly better than the two traditional algorithms. In conclusion, this algorithm can better complete the rapid identification of circuit board defect locations.

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基于机器视觉的线路板焊接缺陷识别算法研究

为了提高电路板的缺陷检测能力，降低漏检率和误检率，提出了一种基于机器视觉的电路板焊接缺陷识别算法。该系统通过x射线源、图像增强器和电荷耦合器件(CCD)获得待测电路板的灰度图像。使用累积采样降噪算法对所有测试图像进行噪声抑制。采用多角度图像采集的标准模板匹配模型实现缺陷识别算法。通过设置最佳模板匹配参数(BTM)，完成测试图像与标准图像的差异区域提取。然后，利用不同视角的标定变换完成缺陷区域特征信息的迭代，提高缺陷检测识别能力;实验在15个不同缺陷类型的电路板图像上进行了测试。结果表明，该算法对桥梁缺陷、偏心缺陷和焊点气泡缺陷的漏检率分别为0.58%、1.18%、1.95%，漏检率分别为0.12%、0.86%、2.34%。它明显优于传统算法。在处理速度和最大适应度方面，该算法也略优于两种传统算法。综上所述，该算法能较好地完成电路板缺陷位置的快速识别。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

IEEE Instrumentation & Measurement Magazine 工程技术-工程：电子与电气

CiteScore

4.20

自引率

4.80%

发文量

147

审稿时长

>12 weeks

期刊介绍： IEEE Instrumentation & Measurement Magazine is a bimonthly publication. It publishes in February, April, June, August, October, and December of each year. The magazine covers a wide variety of topics in instrumentation, measurement, and systems that measure or instrument equipment or other systems. The magazine has the goal of providing readable introductions and overviews of technology in instrumentation and measurement to a wide engineering audience. It does this through articles, tutorials, columns, and departments. Its goal is to cross disciplines to encourage further research and development in instrumentation and measurement.