Amit Thakkar
Papers
1
Total Citations
9
H-Index
1
About
Amit Thakkar is a researcher at the forefront of computer vision and autonomous robotics, with a focused expertise in optimizing real-time object detection systems. His most-cited work, "Optimizing object detection for autonomous robots: a comparative analysis of YOLO models" (2025), has already garnered 9 citations, reflecting its immediate relevance to the field. In this study, Thakkar systematically evaluates and refines You Only Look Once (YOLO) architectures to enhance detection accuracy and computational efficiency for resource-constrained robotic platforms. His contributions are pivotal for enabling safer and more responsive autonomous navigation, particularly in dynamic environments. By bridging the gap between state-of-the-art deep learning models and practical deployment on embedded systems, Thakkar’s research addresses critical challenges in latency and power consumption. His work not only advances the theoretical understanding of model optimization but also provides actionable benchmarks for engineers developing autonomous vehicles, drones, and industrial robots. As a rising voice in applied AI, Thakkar’s findings are already shaping next-generation perception pipelines, making him a key figure to watch in the evolution of intelligent, self-aware machines.
Research Focus
Key Achievements
Top Papers
- 1