Lingding Li

Papers

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About

Lingding Li is a researcher at the forefront of intelligent robotics and computer vision, with a primary focus on structural health monitoring and autonomous systems. Their most notable contribution is the development of a hybrid attention mechanism and RepGFPN method for detecting wall cracks in high-altitude cleaning robots, published in 2024. This work addresses the critical challenge of identifying cracks of varying shapes and scales on building exteriors—a task essential for safety inspections but difficult due to complex visual environments. By integrating the GAM attention mechanism with RepGFPN, Li’s approach significantly enhances detection accuracy and robustness, enabling cleaning robots to perform dual functions of maintenance and defect identification. Although recently published, this paper has already garnered 2 citations, signaling growing interest in their practical, application-driven solutions. Li’s research bridges deep learning and robotics, offering scalable tools for infrastructure maintenance. Their work stands out for its direct industrial relevance, promising to reduce human risk in high-altitude operations while improving building longevity. As a rising voice in autonomous inspection systems, Lingding Li continues to push boundaries in applied AI and robotic perception.