Ling Luo
Papers
2
Total Citations
10
H-Index
2
About
Ling Luo is a researcher at the forefront of applying deep learning and IoT technologies to solve complex industrial and environmental challenges. Her work spans two critical domains: underwater robotics and industrial quality control. In underwater technology, Luo introduced the SVGS-DSGAT framework, an IoT-enabled innovation that significantly improves object detection in high-noise, low-contrast underwater environments—a persistent hurdle for ocean monitoring and resource management. Her second major contribution addresses manufacturing quality, where she developed a deep learning-based method for detecting surface defects in hot-rolled strip steel, particularly targeting the less-studied hot-rolled flattening stage. Both papers have garnered 5 citations each, reflecting their early but meaningful impact in their respective fields. Luo’s research is notable for bridging the gap between theoretical deep learning models and practical, real-world applications—from the murky depths of the ocean to the factory floor. Her work demonstrates a clear commitment to advancing automation and precision in challenging environments, making her a promising voice in applied AI and industrial IoT.
Research Focus
Key Achievements
Top Papers
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- 2