Adarsh Tandiya

Papers

Total Citations

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About

Adarsh Tandiya is a researcher specializing in computer vision, automated quality control, and robotics-based inspection systems, with a particular focus on defect detection in automotive manufacturing environments. His most significant contributions center on the development of deflectometry-based detection systems capable of identifying defects on semi-specular and painted automotive surfaces — a technically challenging domain due to complex surface topologies and variable lighting conditions. Tandiya's most cited work, "An Efficient Automotive Paint Defect Detection System" (2019, 17 citations), introduced a robotic arm-mounted screen-camera setup that brought both precision and adaptability to surface inspection. His earlier 2018 paper (13 citations) laid the methodological groundwork, proposing a hybrid multi-threaded pipeline that significantly improved processing efficiency. His 2020 work (7 citations) further advanced the field by engineering a real-time system robust enough to handle moving parts and surface vibrations — practical constraints critical for real-world industrial deployment. Collectively, Tandiya's research bridges the gap between academic computer vision research and industrial application, offering scalable, intelligent inspection solutions that reduce reliance on manual quality control. His work is of particular relevance to researchers and engineers working at the intersection of robotics, machine vision, and smart manufacturing.