文献类型：期刊文献

中文题名：基于机器视觉的手机异形主板非标自动化检测算法

英文题名：Non-Standard Automatic Detection Algorithm for Mobile Phone Special-Shaped Mainboard Based on Machine Vision

作者：常青[1];张天宇[1];赵冰冰[1]

机构：[1]华东理工大学信息科学与工程学院

年份：2019

卷号：45

期号：4

起止页码：632

中文期刊名：华东理工大学学报（自然科学版）

外文期刊名：Journal of East China University of Science and Technology

收录：CSTPCD;;Scopus;北大核心:【北大核心2017】；CSCD:【CSCD_E2019_2020】；

语种：中文

中文关键词：PCBA图像识别;位置检测;状态判断

外文关键词：PCBA image recognition;station detection;state estimation

摘要：以RF自动测试线中手机异形(L形)单板四连排布在非标自动化测试系统中的图像为研究对象,提出了基于改进的AKAZE特征检测算法,保证了在目标出现尺度、旋转、光照等变化时算法自身有较强的鲁棒性,克服了传统方法在异常情况下特征唯一性及稳定性的局限,实现了对PCBA穴位或治具异常的准确判断.引入支持向量机方法,对工位及治具状态进行判断,通过建立SVM分类模型对已组建的特征样本库进行分类训练,大大改善了目前实际生产中使用的简单分类法的效果.实现了跨平台代码移植,在实际的自动化测试平台环境实现了对PCBA的异常检测,结果显示该算法能够满足实际生产精度和实时性的指标要求.
Printed circuit board assembly (PCBA) production is composed of a few complex processes, including tinning, placement, dispensing, reflow, and other production technologies, e.g., quality and function inspection, in which some processes require customized automation solutions. Moreover, industrial image detection has highly practical requirements in speed and accuracy. However, many theoretic algorithms can not meet the practical requirements. Therefore, it is necessary to select appropriate and effective detection algorithms according to the actual characteristics of PCBA detection images. The objective of this paper is to process the images of non-standard automated test system with four-row mobile phone profiled (L-shaped) veneers in the RF automatic test line. To this end, this paper proposes an improved AKAZE feature detection algorithm to attain a strong robustness when the scale, rotation and illumination change and realize the low feature point description dimension and quick matching, and further overcome the limitation in the uniqueness and stability of conventional methods under abnormal conditions and actualize the accurate judgment of PCBA acupoint or fixture abnormality. Since the traditional gray classification method often suffers from batch classification errors, the support vector machine (SVM) is introduced to judge the station and fixture states. A SVM classification model is established to classify and train the characteristic sample library. The accuracy and timeliness of the training method are verified by using the local sample database until satisfying the application conditions of the existing solutions in the industrial environment, which shows that this proposed method can significantly improve the effect of simple classification method used in actual production, and reduce the occurrence of missed inspection and misjudgment. Finally, the cross-platform code migration is completed and the PCBA anomaly detection is applied to the real industrial environment. It is seen from the results that the proposed algorithm process can achieve the expected research objectives and meet the practical application requirements perfectly.