Chalani Ekanayake
Papers
2
Total Citations
22
H-Index
2
About
Chalani Ekanayake is a researcher focused on the intersection of computer vision and embedded systems, with a key emphasis on real-time object detection for autonomous driving. Her primary research area involves developing efficient deep learning frameworks for traffic sign and traffic light detection, a critical component for safe and reliable autonomous navigation. Ekanayake’s major contribution lies in addressing the fundamental challenge of balancing high detection accuracy with real-time performance on computationally constrained hardware. Her most cited work, "Towards Real-time Traffic Sign and Traffic Light Detection on Embedded Systems" (2022), which has garnered 18 citations, proposes a simple, end-to-end deep learning detection framework designed to operate effectively on embedded platforms. This work is notable for its practical approach, moving beyond accuracy-focused models to deliver solutions that are deployable in real-world, resource-limited environments. By tackling the latency and computational bottlenecks that plague many existing systems, Ekanayake’s research directly contributes to making autonomous driving technology more accessible and robust, bridging the gap between theoretical computer vision and practical, on-vehicle implementation.
Research Focus
Key Achievements
Top Papers
- 1
- 2