Qile Zhang
Papers
2
Total Citations
23
H-Index
2
About
Qile Zhang is a researcher advancing the field of intelligent welding robotics through computer vision and deep learning. His primary research areas include real-time image processing, object detection under challenging conditions, and structured light vision guidance for automated welding systems. Zhang’s major contributions focus on developing robust, high-speed algorithms for detecting weld features and extracting laser stripe regions in noisy industrial environments. His most cited work, "A weld feature points detection method based on improved YOLO for welding robots in strong noise environment" (2022), has garnered 20 citations, demonstrating its significance in enabling precise, real-time robotic welding despite interference from arc light, spatter, and mechanical vibration. Building on this, his 2024 paper on improved YOLO-based laser stripe extraction tackles the critical challenge of balancing detection speed, accuracy, and applicability—key limitations of existing methods. By enhancing region-of-interest extraction for non-standard welding processes, Zhang’s research directly impacts manufacturing efficiency and quality control. His work is notable for bridging the gap between theoretical deep learning models and practical industrial deployment, offering solutions that are both computationally efficient and robust to harsh conditions.
Research Focus
Key Achievements
Top Papers
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