Keypoint-Based Automated Component Placement Inspection for Printed Circuit Boards

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 the locations and sizes of different components. They are object detection algorithms based on key points 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 to produce 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 to effectively explore features of different components for accurate component inspection. The proposed algorithm has the advantages of simplicity in training for data collection, high accuracy in defect detection, and high reusability and flexibility for online inspection. The algorithm is an effective alternative for automated inspection in smart factories, with growing demand for product quality and diversification.