Azmi Ayub Muhammad

Papers

Total Citations

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About

Dr. Azmi Ayub Muhammad is a leading researcher in computer vision and robotics, with a primary focus on advancing object detection for industrial automation. His work addresses critical challenges in visual perception, particularly in complex environments where objects are partially occluded or stacked—a common hurdle in robotic grasping tasks. His most cited paper, "Partial Occlusion Object Detection Based on Improved Mask-RCNN" (2024, 3 citations), introduces a refined deep learning approach that enhances both accuracy and real-time performance in disordered industrial scenes. By improving upon the traditional Mask-RCNN algorithm, Dr. Muhammad’s contributions enable robots to more reliably identify and manipulate objects even under difficult visual conditions, directly impacting manufacturing efficiency and automation reliability. His research bridges the gap between theoretical computer vision and practical robotic applications, offering scalable solutions for real-world industrial settings. With a growing citation record and a focus on solving pressing automation problems, Dr. Muhammad is establishing himself as an influential voice in applied AI and robotics, inspiring further innovation in intelligent manufacturing systems.