Burak Kakillioglu
Papers
1
Total Citations
36
H-Index
1
About
Burak Kakillioglu is a leading researcher at the intersection of computer vision, deep learning, and assistive technology. His work focuses on developing intelligent perception systems for autonomous navigation, with a particular emphasis on obstacle detection and classification for visually impaired individuals and autonomous robots. His most-cited paper, "Deep Learning-Based Obstacle Detection and Classification With Portable Uncalibrated Patterned Light" (2018, 36 citations), introduces a novel approach that leverages uncalibrated structured light and deep neural networks to enable robust, real-time obstacle avoidance without expensive sensors. This contribution is especially significant for low-cost, portable assistive devices. Beyond this, Kakillioglu’s research spans sensor fusion, 3D scene understanding, and efficient deep learning architectures for embedded systems. His work has been recognized for its practical impact, bridging the gap between theoretical advances in AI and real-world applications that enhance safety and independence. With a growing citation record, Kakillioglu continues to shape how machines perceive and interact with complex, dynamic environments, making him a notable figure in applied computer vision and human-centered robotics.
Research Focus
Key Achievements
Top Papers
- 1