Diana Cordova-Esparza
Papers
1
Total Citations
189
H-Index
1
About
Diana Cordova-Esparza is a leading researcher in computer vision and deep learning, with a primary focus on real-time object detection systems. Her most significant contribution is the comprehensive survey "A Comprehensive Review of YOLO Architectures in Computer Vision: From YOLOv1 to YOLOv8 and YOLO-NAS," which has garnered 189 citations and become an essential reference in the field. This work meticulously traces the evolution of the YOLO (You Only Look Once) family of algorithms, which are foundational for applications in robotics, autonomous vehicles, and video surveillance. By systematically analyzing each iteration from YOLOv1 through YOLOv8 and YOLO-NAS, Cordova-Esparza has provided researchers and practitioners with a clear roadmap of architectural innovations and performance improvements. Her work not only synthesizes technical advancements but also highlights practical trade-offs between speed and accuracy, making it invaluable for engineers deploying real-time detection systems. Through this landmark review, Cordova-Esparza has established herself as a key voice in advancing accessible, high-performance computer vision solutions, directly impacting how modern AI systems perceive and interact with the visual world.
Research Focus
Key Achievements
Top Papers
- 1