Chung, S.-T.; Hwang, W.-J.; Tai, T.-M. Keypoint-Based Automated Component Placement Inspection for Printed Circuit Boards. Appl. Sci.2023, 13, 9863.
Chung, S.-T.; Hwang, W.-J.; Tai, T.-M. Keypoint-Based Automated Component Placement Inspection for Printed Circuit Boards. Appl. Sci. 2023, 13, 9863.
Chung, S.-T.; Hwang, W.-J.; Tai, T.-M. Keypoint-Based Automated Component Placement Inspection for Printed Circuit Boards. Appl. Sci.2023, 13, 9863.
Chung, S.-T.; Hwang, W.-J.; Tai, T.-M. Keypoint-Based Automated Component Placement Inspection for Printed Circuit Boards. Appl. Sci. 2023, 13, 9863.
Abstract
This study aims to develop novel automated computer vision algorithms and systems for component replacement inspection for Printed Circuit Boards (PCBs). The proposed algorithms are able to identify locations as well as sizes of different components. They are object detection algorithms based on keypoints of the target components. The algorithms can be implemented as neural networks consisting of two portions: frontend networks and backend networks. The frontend networks are used for the feature extractions of input images. The backend networks are adopted for producing component inspection results. Each component class can has its own frontend and backend networks. In this way, the neural model for the component class can be effectively reused for different PCBs. To reduce the computation time for the inference of the networks, different component classes can share the same frontend networks. A two-stage training process is proposed for effectively exploring features of different components for accurate component inspection. The proposed algorithm has the advantages of the simplicity for training data collection, high accuracy for defect detection, and high reusability and flexibility for online inspection. The algorithm is an effective alternative for the automated inspection in smart factory with growing demand for product quality and diversification.
Keywords
Object Detection; Artificial Intelligence; Neural Networks; Internet of Things; Component Placement Inspection
Subject
Computer Science and Mathematics, Computer Vision and Graphics
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
