Fang Luo

Fang Luo serves as a Professional Teacher of electrical automation technology at the School of Mechatronics and Automotive Engineering, Qingyuan Polytechnic in Guangdong, China. With a strong research focus bridging industrial applications and affective computing, she maintains active collaborations across multiple institutions as evidenced by her 26 co-authors.

Her educational background includes:

• M.S. in Control Theory and Control Engineering from Guangdong University of Technology (2013)

Dr. Luo's research spans two primary domains with significant industrial impact. In machine vision, she develops innovative neural network architectures for industrial defect detection, addressing challenges like low-contrast defects in metal parts and magnetic tiles. Her technical contributions include attention mechanisms, multi-view analysis, and scale-adaptive networks that improve precision in manufacturing quality control. Simultaneously, she explores affective computing applications in educational contexts, developing methods for student anxiety assessment and shyness trait recognition using multi-modal data. Her work demonstrates exceptional versatility across computer vision applications while maintaining practical relevance to industry and education.

Her scientific contributions are reflected in 7 publications with 36 citations between 2022-2025, primarily in IEEE journals including IEEE Transactions on Affective Computing and IEEE Access. Her research addresses critical challenges in industrial automation and educational technology, with particular focus on improving detection accuracy for small defects in metal components and developing non-invasive methods for student emotional assessment.

As an educator and researcher, Dr. Luo supervises academic projects in electrical automation technology while maintaining active research collaborations. Her work bridges theoretical neural network advancements with practical industrial implementations, particularly in manufacturing quality assurance systems. She also contributes to educational technology through research on student emotional states in online learning environments.

Her laboratory work focuses on computer vision applications for industrial inspection systems, developing neural network architectures optimized for defect detection in challenging manufacturing environments. These systems address specific industry pain points including low-contrast defects, small anomaly regions, and complex background interference that traditional inspection methods struggle to resolve.