Chongben Ni
Papers
2
Total Citations
8
H-Index
2
About
Chongben Ni is a researcher advancing intelligent automation in shipbuilding, with a focus on vision-guided robotic systems for welding and assembly. His key research areas include computer vision, point cloud processing, and autonomous manufacturing for industrial applications. Ni’s major contributions center on developing robust line segment detection algorithms tailored to complex shipyard environments. His 2021 paper, “Grid Based Line Segment Detector and Application: Vision System for Autonomous Ship Small Assembly Line,” introduced a vision system that enables welding robots to operate without laborious pre-teaching, directly addressing the shipbuilding industry’s need for flexible automation. Building on this, his 2024 work, “Run Length Encoding Based Weld Seam Detection from Point Clouds of Ship Stiffened Panel,” presents an innovative Run Length Encoding-based Line Segment Detector (RSD) that efficiently extracts weld seams from 3D point clouds, a critical step for autonomous welding in stiffened panel structures. Both papers have garnered 4 citations each, reflecting their emerging impact in the niche field of maritime robotics. Ni’s work is notable for bridging the gap between theoretical computer vision and practical, heavy-industry applications, offering scalable solutions that reduce human intervention in hazardous shipyard tasks. His research holds promise for transforming ship manufacturing through intelligent, data-driven automation.
Research Focus
Key Achievements
Top Papers
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- 2