Tinglong Tang
Papers
4
Total Citations
49
H-Index
4
About
Tinglong Tang is a leading researcher in intelligent robotics and industrial automation, with a focus on integrating deep learning and embedded systems for real-world manufacturing applications. His work addresses critical challenges in defect detection, autonomous control, and anomaly recognition, bridging the gap between advanced algorithms and practical deployment on resource-constrained hardware. Tang’s most-cited paper, “Surface Defect Detection of Hot Rolled Steel Based on Attention Mechanism and Dilated Convolution for Industrial Robots” (2023, 17 citations), introduces a novel approach that enhances accuracy in dual-task defect detection, a key bottleneck in steel manufacturing. His 2021 study on “Intelligent control of quad-rotor aircrafts with a STM32 microcontroller using deep neural networks” (16 citations) demonstrates how to deploy neural networks on embedded devices, reducing latency and energy consumption for drone control. Tang also pioneered the “double encoder–decoder generative adversarial networks” for anomaly detection (2020, 9 citations), enabling robots to identify rare defects in industrial environments. His work on robot vision implementation with digital signal processors (2020, 7 citations) provides a hardware framework that accelerates embedded vision applications. With a growing citation impact, Tang’s research is shaping the future of smart manufacturing and autonomous systems.
Research Focus
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Top Papers
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