Tharindu Wickremasinghe
Papers
2
Total Citations
22
H-Index
2
About
Tharindu Wickremasinghe is a researcher focused on the intersection of computer vision and embedded systems, with a particular emphasis on real-time object detection for autonomous driving. His primary research areas include deep learning-based traffic sign and traffic light detection, and the optimization of these models for deployment on resource-constrained hardware. Wickremasinghe’s major contribution lies in addressing a critical gap in the field: while many detection systems achieve high accuracy in complex scenarios, they often fail to deliver real-time performance on embedded platforms. His work proposes a simple, end-to-end deep learning framework that balances detection accuracy with computational efficiency, enabling practical use in vehicles and other edge devices. His most-cited paper, "Towards Real-time Traffic Sign and Traffic Light Detection on Embedded Systems" (2022), has accumulated 18 citations, reflecting its growing influence in the autonomous driving and embedded AI communities. This work is notable for its direct focus on real-world deployment challenges, making it a valuable resource for researchers and engineers seeking to bridge the gap between algorithmic performance and hardware limitations.
Research Focus
Key Achievements
Top Papers
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- 2