Honghui Fan
Papers
1
Total Citations
5
H-Index
1
About
Honghui Fan is a researcher at the forefront of intelligent manufacturing and quality control, with a primary focus on automated defect detection for printed circuit boards (PCBs) using advanced deep learning and computer vision techniques. Their most cited work introduces a novel method that combines the lightweight MobileNet algorithm with an improved YOLOv4 model to identify and classify PCB defects with high efficiency and accuracy. This contribution is particularly significant given the rising complexity of PCBs in emerging 3C products like smartwatches and wearable devices, where traditional inspection methods fall short. With 5 citations, this paper demonstrates early impact in a rapidly evolving field. Fan’s research bridges the gap between industrial quality assurance and state-of-the-art artificial intelligence, offering practical solutions that enhance production reliability and reduce manual inspection costs. Their work is a valuable resource for students and engineers exploring real-world applications of object detection and edge computing in electronics manufacturing.
Research Focus
Key Achievements
Top Papers
- 1