Fast and Precise Positioning in PCBs Using Deep Neural Network Regression

• Published in: IEEE transactions on instrumentation and measurement Vol. 69; no. 7; pp. 4692 - 4701
• Main Authors: Tsai, Du-Ming, Chou, Yi-Hsiang
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Alignment; Artificial neural networks; Circuit boards; Computational modeling; Convolutional neural network (CNN); deep learning; Feature extraction; Illumination; Image processing; Image registration; Inspection; Machine learning; Multilayer perceptrons; Neural networks; positioning; printed circuit boards (PCBs); Printed circuits; Regression analysis; Robustness (mathematics); Support vector machines; Template matching; Training; vision-based measurement
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2019.2957866

Precision positioning is a very important task for automatic assembly and inspection in the manufacturing process. The conventional image processing for image alignment has relied on template matching, which is computationally intensive for objects in arbitrary locations and orientations. In this article, we propose the deep neural network regressors for fast and accurate image alignment. They are especially applied to the positioning of printed circuit boards (PCBs). The simple multilayer perceptron (MLP), the convolutional neural network (CNN), and the CNN models incorporated with support vector regression (SVR) are proposed and evaluated for the PCB positioning task. The proposed deep neural networks require only one single reference sample with a manually marked template window. All training images and the ground-truth geometric parameters are automatically generated for the model training. The effect of illumination changes and the strategies to cope with lighting variations are analyzed and proposed for robust positioning. Experimental results indicate that the proposed regressors can achieve a subpixel accuracy in translation and yield a rotation error less than 1° with 1-ms evaluation time for PCB positioning.