Mazleenda Mazni

Papers

Total Citations

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About

Mazleenda Mazni is a robotics researcher whose work focuses on the development of intelligent inspection systems, particularly wall-climbing robots for structural health monitoring. Her key research areas include robotic adhesion mechanisms, real-time defect detection, and the integration of deep learning for autonomous infrastructure assessment. Mazni’s major contributions center on enabling robots to navigate vertical surfaces while simultaneously classifying structural defects. Her 2023 paper, "Real-Time Crack Classification with Wall-Climbing Robot Using MobileNetV2," demonstrates a practical fusion of lightweight convolutional neural networks with robotic mobility, achieving efficient on-board crack detection—a critical step toward automated building inspections. This work has garnered 4 citations, reflecting its relevance in the growing field of AI-driven robotics. Additionally, her 2022 study, "Analysis of the Hybrid Adhesion Mechanism of the Wall Climbing Robot," with 3 citations, provides foundational insights into combining suction and magnetic adhesion for versatile surface traversal. Mazni’s research is notable for bridging mechanical design and machine learning, offering scalable solutions for aging infrastructure. Her achievements highlight a commitment to creating robust, real-world robotic systems that enhance safety and reduce human inspection risks.