Daniel Moreano

Papers

Total Citations

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About

Daniel Moreano is a researcher at the forefront of intelligent robotics and computer vision, with a focus on integrating deep learning into automated systems. His most cited work, "Color Classification Using a 3-DOF Robotic Arm Based on the YOLOv5 Model" (2024), demonstrates a novel approach to merging robotic manipulation with state-of-the-art object detection. By implementing a 3-degree-of-freedom robotic arm that leverages the YOLOv5 deep learning architecture, Moreano has shown how real-time color classification can be achieved with high precision, bridging the gap between perception and action in industrial automation. This contribution, already garnering early citations, highlights his ability to apply advanced AI models to practical, hardware-driven challenges. Moreano’s research is particularly impactful for students and engineers exploring the intersection of robotics, computer vision, and deep learning, offering a scalable framework for tasks like sorting and quality control. His work underscores a commitment to making automation more intelligent and accessible, positioning him as an emerging voice in the field of applied robotics.