Leonardo Agnusdei
Papers
1
Total Citations
11
H-Index
1
About
Leonardo Agnusdei is a researcher at the forefront of applying machine vision and deep learning to industrial manufacturing challenges. His work centers on computer vision, defect detection, and the integration of advanced neural network architectures into production quality control. Agnusdei’s most cited study, “Application of Mask R-CNN and YOLOv8 algorithms for defect detection in printed circuit board manufacturing” (2025, 11 citations), demonstrates how modern machine learning algorithms can significantly enhance automated inspection in electronics production. By systematically comparing state-of-the-art instance segmentation and object detection models, he provides a practical roadmap for manufacturers seeking to leverage hardware improvements and AI for higher accuracy and efficiency. This research has immediate relevance to Industry 4.0 initiatives, where reducing human error and increasing throughput are paramount. Agnusdei’s contributions bridge the gap between cutting-edge computer vision research and real-world industrial applications, making his work valuable for both engineers implementing quality systems and students exploring the intersection of AI and manufacturing. His focus on benchmarking and application-oriented studies positions him as a key voice in the ongoing digital transformation of production environments.
Research Focus
Key Achievements
Top Papers
- 1