Quan Jiang

Jiangsu University of Science and Technology

Papers

Total Citations

H-Index

About

Quan Jiang is an emerging researcher whose work sits at the intersection of computer vision and environmental monitoring. His notable contribution centers on the development of a tree detection algorithm leveraging an embedded YOLO (You Only Look Once) lightweight network architecture, published in 2022. This work addresses a practical and pressing challenge in precision forestry and ecological surveillance — accurately detecting trees in complex natural environments while maintaining computational efficiency suitable for embedded and edge computing systems. By adapting the YOLO framework into a lightweight configuration, Jiang's approach makes real-time tree detection feasible on resource-constrained hardware, broadening the accessibility of automated vegetation monitoring tools. Though early in its citation trajectory with 3 citations to date, the research taps into rapidly growing demand for AI-driven environmental sensing solutions, positioning it for increasing relevance as smart forestry and automated land-use analysis gain momentum. Jiang's work reflects a promising research direction that bridges deep learning innovation with applied ecological technology, suggesting a researcher who is actively contributing to solutions at the frontier of intelligent environmental systems.