Search Results for author: Yanlong Cao

Found 8 papers, 1 papers with code

Learning Inter- and Intraframe Representations for Non-Lambertian Photometric Stereo

no code implementations26 Dec 2020 Yanlong Cao, Binjie Ding, Zewei He, Jiangxin Yang, Jingxi Chen, Yanpeng Cao, Xin Li

Photometric stereo provides an important method for high-fidelity 3D reconstruction based on multiple intensity images captured under different illumination directions.

3D Reconstruction

Boosting Image Super-Resolution Via Fusion of Complementary Information Captured by Multi-Modal Sensors

no code implementations7 Dec 2020 Fan Wang, Jiangxin Yang, Yanlong Cao, Yanpeng Cao, Michael Ying Yang

Image Super-Resolution (SR) provides a promising technique to enhance the image quality of low-resolution optical sensors, facilitating better-performing target detection and autonomous navigation in a wide range of robotics applications.

3D Reconstruction Autonomous Navigation +1

Deep Neural Network for Fast and Accurate Single Image Super-Resolution via Channel-Attention-based Fusion of Orientation-aware Features

no code implementations9 Dec 2019 Du Chen, Zewei He, Yanpeng Cao, Jiangxin Yang, Yanlong Cao, Michael Ying Yang, Siliang Tang, Yueting Zhuang

Firstly, we proposed a novel Orientation-Aware feature extraction and fusion Module (OAM), which contains a mixture of 1D and 2D convolutional kernels (i. e., 5 x 1, 1 x 5, and 3 x 3) for extracting orientation-aware features.

Computational Efficiency Image Super-Resolution

Unsupervised Domain Adaptation for Multispectral Pedestrian Detection

no code implementations7 Apr 2019 Dayan Guan, Xing Luo, Yanpeng Cao, Jiangxin Yang, Yanlong Cao, George Vosselman, Michael Ying Yang

In this paper, we propose a novel unsupervised domain adaptation framework for multispectral pedestrian detection, by iteratively generating pseudo annotations and updating the parameters of our designed multispectral pedestrian detector on target domain.

Autonomous Driving Pedestrian Detection +1

Box-level Segmentation Supervised Deep Neural Networks for Accurate and Real-time Multispectral Pedestrian Detection

no code implementations14 Feb 2019 Yanpeng Cao, Dayan Guan, Yulun Wu, Jiangxin Yang, Yanlong Cao, Michael Ying Yang

Effective fusion of complementary information captured by multi-modal sensors (visible and infrared cameras) enables robust pedestrian detection under various surveillance situations (e. g. daytime and nighttime).

Autonomous Driving Computational Efficiency +1

Fusion of Multispectral Data Through Illumination-aware Deep Neural Networks for Pedestrian Detection

no code implementations27 Feb 2018 Dayan Guan, Yanpeng Cao, Jun Liang, Yanlong Cao, Michael Ying Yang

Moreover, we utilized illumination information together with multispectral data to generate more accurate semantic segmentation which are used to boost pedestrian detection accuracy.

Autonomous Driving Multi-Task Learning +2

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