M. Buana Fashla

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About

M. Buana Fashla is a robotics and computer vision researcher whose work bridges real-time object detection and autonomous systems, with a particular focus on mobile soccer robots. His most cited paper, "The Real-Time Object Detection System on Mobile Soccer Robot using YOLO v3" (2019, 16 citations), marks a pivotal contribution to the field by replacing traditional color-filtering methods with neural network-based detection. This work enables robots to reliably identify critical game elements—balls, goals, and field lines—under dynamic, real-world conditions, advancing the capabilities of autonomous agents in competitive environments. Fashla’s research addresses a fundamental challenge in robotics: achieving fast, accurate perception with limited onboard computational resources. By integrating YOLO v3 into mobile platforms, he demonstrated that deep learning could be effectively deployed in resource-constrained systems, influencing subsequent work in embedded vision and robot navigation. His contributions have implications beyond soccer robotics, extending to areas like industrial automation and autonomous vehicles. With 16 citations on this key paper alone, Fashla’s work continues to inspire researchers seeking efficient, real-time solutions for object detection in mobile robotics, solidifying his reputation as an innovator at the intersection of neural networks and practical autonomous systems.