Lin Mao

Papers

Total Citations

H-Index

About

Lin Mao is a researcher whose work lies at the intersection of computer vision and deep learning, with a particular focus on object detection and feature extraction. His most notable contribution is the development of the "Pyramid Frequency Feature Fusion Object Detection Network," a 2021 study that addresses a critical limitation in deep learning architectures: the loss of high-frequency texture details during up-sampling operations. By proposing a novel pyramid network that fuses frequency-domain features, Mao’s work enhances the ability of detection models to preserve fine-grained visual information, leading to more accurate object recognition. This research has garnered 4 citations, reflecting its emerging impact in the field. Mao’s contributions are particularly relevant for applications requiring precise visual analysis, such as autonomous systems and surveillance. His work demonstrates a keen understanding of how to bridge the gap between theoretical feature representation and practical detection performance, marking him as a thoughtful contributor to the ongoing evolution of deep learning-based vision systems.