Muhammad Hasan Dzulfadli
Papers
1
Total Citations
1
H-Index
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About
Muhammad Hasan Dzulfadli is a researcher advancing the frontier of autonomous underwater robotics, with a focus on computer vision and intelligent navigation systems. His most cited work, "Autonomous Underwater Vehicle for Pipeline Following Using YOLOv8 Instance Segmentation and Robotic Operating System" (2024), addresses a critical challenge in subsea infrastructure inspection: enabling AUVs to autonomously track pipelines with precision. By integrating YOLOv8 instance segmentation with ROS, Dzulfadli’s system reduces human operator burden while improving navigation accuracy in complex underwater environments. This contribution has already garnered attention in the field, with citations reflecting its relevance to both marine robotics and industrial automation. Dzulfadli’s research sits at the intersection of deep learning, real-time object detection, and autonomous systems, offering practical solutions for offshore oil and gas, environmental monitoring, and underwater archaeology. His work exemplifies how modern AI techniques can be deployed in resource-constrained, dynamic underwater settings. As a rising voice in autonomous systems, Dzulfadli continues to push the boundaries of what AUVs can achieve, making underwater inspection safer, more efficient, and increasingly autonomous.
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Top Papers
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