Papers
3
Total Citations
213
H-Index
3
About
Quan Zhou is a researcher whose work sits at the intersection of computer vision, deep learning, and real-time intelligent systems, with a particular focus on semantic segmentation and object detection for safety-critical applications. His most influential contribution, AGLNet (2020), introduced an attention-guided lightweight network architecture designed to achieve real-time semantic segmentation of self-driving imagery — a paper that has garnered over 129 citations and established him as a notable voice in efficient neural network design. Building on this foundation, his 2024 work on boundary-guided lightweight semantic segmentation further advances the field by leveraging multi-scale semantic context and dual-resolution networks to better capture image details and semantics, accumulating 65 citations in a short period and demonstrating sustained relevance in multimedia applications ranging from autonomous driving to augmented reality. Zhou has also extended his expertise to industrial safety domains, contributing an improved YOLOv5-based detection system for identifying foreign objects and power component defects on high-voltage transmission lines. Collectively, his research reflects a consistent drive to make computer vision systems both computationally efficient and practically deployable across diverse real-world environments.
Research Focus
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