Yuan Duan

Papers

Total Citations

152

H-Index

About

Yuan Duan is a leading researcher in the field of intelligent infrastructure assessment, with a primary focus on automated pavement crack detection and segmentation using deep learning. Their major contributions center on developing efficient, end-to-end neural network architectures that address the critical challenge of accurately identifying small or subtle pavement cracks often missed due to background interference. Duan’s most influential work, a 2023 paper on a lightweight encoder-decoder network for automatic pavement crack detection, has garnered 144 citations, underscoring its significant impact on practical road condition evaluation. This work is complemented by their earlier 2022 proposal of RHA-Net, an encoder-decoder network integrating residual blocks and hybrid attention mechanisms to enhance crack segmentation accuracy. Together, these contributions demonstrate Duan’s commitment to balancing computational efficiency with high precision, making their methods suitable for real-world deployment. Their research is vital for transportation agencies seeking to automate pavement surveys, reduce maintenance costs, and improve road safety. Duan’s innovative approach to combining lightweight design with advanced attention mechanisms positions them as a key figure in advancing smart infrastructure monitoring.