Vecky Canisius Poekoel

Papers

Total Citations

H-Index

About

Vecky Canisius Poekoel is a researcher at the forefront of applying deep learning to marine conservation, with a specialized focus on sea turtle detection. His work directly addresses the critical need for automated, real-time monitoring systems to protect vulnerable sea turtle habitats. Poekoel’s major contributions lie in developing efficient neural network architectures that operate effectively on underwater robots, enabling non-intrusive observation of turtle behavior. His most cited paper, "Streamlining Deep Learning Network for Real-time Sea Turtle Detection" (2024, 4 citations), pioneers a streamlined model that balances accuracy with the computational constraints of robotic platforms. A subsequent work, "An Efficient and Effective Sea Turtle Detection Using Positioning Enhancement Module" (2024, 1 citation), tackles persistent challenges in underwater computer vision, including background interference, occlusion, and small object detection. By integrating a positioning enhancement module, Poekoel improves detection reliability in complex marine environments. His research directly supports conservation efforts by providing the foundational detection step for subsequent behavioral analysis. Poekoel’s work is notable for bridging the gap between advanced deep learning and practical ecological monitoring, offering a scalable solution for automated wildlife observation that minimizes human disruption to natural ecosystems.