Daniel Souza

Papers

Total Citations

H-Index

About

Daniel Souza is a researcher specializing in the intersection of robotics, computer vision, and materials science, with a particular focus on automated inspection and quality control in manufacturing. His most cited work, "A robotic passive vision system for texture analysis in weld beads" (2022), introduces a novel approach to non-destructive evaluation, leveraging passive imaging and texture analysis to assess weld integrity without active sensors. This contribution addresses a critical challenge in industrial automation, offering a cost-effective and reliable method for real-time defect detection. While his citation count is still emerging, with this paper garnering 3 citations, Souza’s work lays a foundational framework for integrating vision-based systems into robotic welding processes, enhancing precision and reducing human error. His research holds promise for advancing smart manufacturing and Industry 4.0 applications, where automated quality assurance is paramount. As a rising voice in the field, Souza’s contributions are poised to influence future developments in robotic perception and industrial inspection, making him a researcher to watch for students and professionals interested in the practical deployment of computer vision in harsh environments.