Robust and Automatic Visual Guidance Framework for Component Localization on Printed Circuit Boards

Graphs are widely recognized for their robust ability to represent objects through nodes and edges. This article aims to provide stable visual guidance for operators to identify electronic components on printed circuit board (PCB) images during the manufacturing and maintenance processes, using graph techniques to enhance the efficiency of component localization. A three-stage approach is proposed to achieve this objective. First, a method based on graph neural networks is introduced for electronic component detection, trained exclusively on the public FISC-PCB dataset. Second, the registration of the physical location with PCB images is modeled as a Markov Chain, and a fast subgraph matching algorithm is proposed to reduce time and space complexities by leveraging a second-order attribute similarity approximation. Finally, the random sample consensus algorithm is employed to eliminate outliers and calculate geometric relationships, enabling accurate positioning. The proposed framework is evaluated using ten PCB images captured by line-scan cameras or mobile phone cameras and compared with leading contemporary methods. The various experimental results demonstrate that the proposed method outperforms existing state-of-the-art solutions in localization accuracy and meets the requirements of efficiency and space utilization.