Department of Computer Science and Information Engineering, National Cheng Kung University; No.1 University Road, Tainan City 701, Taiwan.
Department of Applied Mathematics, National Chung Hsing University, No. 145, Xingda Road, Taichung City 402, Taiwan.
Sensors (Basel). 2020 May 11;20(9):2736. doi: 10.3390/s20092736.
The fiducial-marks-based alignment process is one of the most critical steps in printed circuit board (PCB) manufacturing. In the alignment process, a machine vision technique is used to detect the fiducial marks and then adjust the position of the vision system in such a way that it is aligned with the PCB. The present study proposed an embedded PCB alignment system, in which a rotation, scale and translation (RST) template-matching algorithm was employed to locate the marks on the PCB surface. The coordinates and angles of the detected marks were then compared with the reference values which were set by users, and the difference between them was used to adjust the position of the vision system accordingly. To improve the positioning accuracy, the angle and location matching process was performed in refinement processes. To overcome the matching time, in the present study we accelerated the rotation matching by eliminating the weak features in the scanning process and converting the normalized cross correlation (NCC) formula to a sum of products. Moreover, the scanning time was reduced by implementing the entire RST process in parallel on threads of a graphics processing unit (GPU) by applying hash functions to find refined positions in the refinement matching process. The experimental results showed that the resulting matching time was around 32× faster than that achieved on a conventional central processing unit (CPU) for a test image size of 1280 × 960 pixels. Furthermore, the precision of the alignment process achieved a considerable result with a tolerance of 36.4μm.
基于基准标记的对准过程是印刷电路板（PCB）制造中最关键的步骤之一。在对准过程中，机器视觉技术用于检测基准标记，然后调整视觉系统的位置，使其与 PCB 对准。本研究提出了一种嵌入式 PCB 对准系统，其中采用旋转、缩放和平移（RST）模板匹配算法来定位 PCB 表面上的标记。然后将检测到的标记的坐标和角度与用户设置的参考值进行比较，并使用它们之间的差值相应地调整视觉系统的位置。为了提高定位精度，在细化过程中执行角度和位置匹配过程。为了克服匹配时间的问题，在本研究中，我们通过在扫描过程中消除弱特征并将归一化互相关（NCC）公式转换为乘积之和，加速了旋转匹配。此外，通过在图形处理单元（GPU）的线程上并行执行整个 RST 过程，并在细化匹配过程中应用哈希函数来找到细化位置，从而减少了扫描时间。实验结果表明，对于大小为 1280×960 像素的测试图像，匹配时间比传统中央处理器（CPU）快约 32 倍。此外，对准过程的精度达到了相当高的水平，容差为 36.4μm。
