Shwetha Prabhu
Papers
1
Total Citations
10
H-Index
1
About
Shwetha Prabhu is a researcher at the forefront of making deep learning practical for resource-constrained environments, with a primary focus on computer vision, edge AI, and IoT systems. Her most cited work, "Exploratory Data Preparation and Model Training Process for Raspberry Pi-Based Object Detection Model Deployments" (2024, 10 citations), addresses a critical gap in the field: the performance degradation of pretrained models when deployed on low-cost, low-quality camera hardware common in robotics and IoT applications. Prabhu’s key contribution lies in developing systematic data preparation and fine-tuning strategies that bridge the domain shift between high-quality training datasets and real-world, noisy input. This work has significant implications for accessible AI, enabling effective object detection on devices like the Raspberry Pi without requiring expensive sensors. Her research empowers hobbyists, students, and engineers to build robust vision systems for autonomous robots, smart agriculture, and environmental monitoring. By focusing on practical deployment challenges, Prabhu is helping democratize computer vision, making it more reliable and affordable for real-world applications.
Research Focus
Key Achievements
Top Papers
- 1