Yanlong Cao

Papers

Total Citations

301

H-Index

About

Yanlong Cao is a leading researcher in computer vision and sensor fusion, with a focus on multispectral imaging and deep learning. His work bridges the gap between visible, infrared, and thermal data to create more robust perception systems for autonomous systems, surveillance, and robotics. Cao’s most cited paper, “Pedestrian detection with unsupervised multispectral feature learning using deep neural networks” (145 citations), introduced a novel approach to learning cross-spectral features without manual labels, significantly advancing night-time detection. He further pushed the boundaries of image enhancement with “Cascaded Deep Networks With Multiple Receptive Fields for Infrared Image Super-Resolution” (103 citations), addressing the high cost and fabrication difficulty of high-resolution infrared detectors. In “Depth and thermal sensor fusion to enhance 3D thermographic reconstruction” (53 citations), Cao developed a mobile, real-time method for generating 3D models with integrated temperature data, with applications ranging from medical imaging to energy auditing. His research is characterized by practical, deployable solutions that fuse multiple sensing modalities, making him a key contributor to the next generation of intelligent, multispectral perception systems.