Max Robotics — Global Service Robot Directory + US Certification

About

Lin Hong’s research lies at the intersection of computer vision, swarm robotics, and safety-critical control, with a particular focus on enabling intelligent systems to perceive and act in complex, unstructured environments. His most impactful contribution is the creation of the **USOD10K benchmark dataset** for underwater salient object detection (2023, 87 citations), which provided the first large-scale, pixel-wise annotated resource for this emerging field—a foundational tool that has accelerated research in underwater visual tasks. In swarm robotics, Hong has explored innovative communication and control paradigms, such as the **Decay Small-World (D-World)** model for optimizing knowledge synchronization under communication constraints, and a **cellular reaction gene regulation network** for self-organized pattern formation, both published in 2022. His recent work (2025) integrates saliency detection with dynamic control barrier functions for real-time, safety-critical obstacle avoidance in mobile robots, demonstrating a practical synthesis of perception and control. With a growing citation footprint, Hong’s research is distinguished by its dual focus on foundational datasets and novel algorithmic frameworks, bridging the gap between theoretical swarm intelligence and real-world robotic deployment.