Prof. Xi Wu Profile

Prof. Xi Wu

Associate Professor at Chengdu Univ of Information Technology

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Publications (6)

Proceedings Article | 26 May 2023 Paper

Research on UAV video motion target detection method based on optical flow network

Zhuoyao Li, Xi Wu, Jinrong Hu, Ye Zhu, Ying Fu

Proceedings Volume 12700, 127003B (2023) https://doi.org/10.1117/12.2682458

KEYWORDS: Target detection, Optical flow, Video, Feature extraction, Optical networks, Unmanned aerial vehicles, Visualization, Video processing

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Motion target detection is a prerequisite for road monitoring, motion target tracking, instance segmentation and other tasks. UAV video images are easily affected by some unavoidable factors in the acquisition process, such as wind interference and own motion during the shooting process can lead to image background changes, target scale changes and intermittent motion, making the motion target detection task more challenging. To address the problems of poor accuracy of existing UAV video motion target detection methods based on deep optical flow networks and the limitation of target detection performance in complex scenes due to the complex and diverse features of UAV video data, this paper proposes a new UAV video motion target detection method based on optical flow networks. Firstly, a convolutional structure reparameterization method is used in the coding part to further fuse detailed and semantic information to improve the feature expression capability of video images; secondly, the self-attentive global motion feature enhancement module proposed in this paper is introduced to improve the network's ability to extract global information and better combine contextual information to achieve more accurate optical flow estimation; finally, the optical flow threshold segmentation is used to obtain different motion target detection results for different scenes by optical flow threshold segmentation. In this paper, three sets of low-altitude UAV video data from different scenes are selected for experiments on the public dataset AU-AIR2019, and the experimental results prove that the proposed method can achieve better motion target detection results in single-target, multi-target and occluded target scenes, and it is better than the current mainstream optical flow networks: FlowNet1, PWC-Net, HD3, PWC-Net and HD3 on the public dataset FlyingChairs. PWC-Net, HD3, GMA metrics EPE (end point error, EPE) on the public dataset FlyingChairs, and improves the RAFT by 0.10 over the benchmark network in this paper, which effectively improves the accuracy of UAV video motion target detection by deep optical flow networks.

SPIE Journal Paper | 22 April 2022

Deep iterative residual back-projection networks for single-image super-resolution

Chuan Tian, Jing Hu, Xi Wu, Wu Wen

JEI, Vol. 31, Issue 02, 023034, (April 2022) https://doi.org/10.1117/12.10.1117/1.JEI.31.2.023034

KEYWORDS: Super resolution, Lawrencium, Feature extraction, Performance modeling, Reconstruction algorithms, Convolution, Visualization, Image processing, RGB color model, Visual process modeling

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Proceedings Article | 29 August 2016 Paper

PSF estimation for blind motion deblurring with image edge prior

Ying Fu, Jin Rong Hu, Xi Wu, Ji Liu Zhou

Proceedings Volume 10033, 1003324 (2016) https://doi.org/10.1117/12.2244846

KEYWORDS: Point spread functions, Image restoration, Motion estimation, Image analysis, Image processing, Image enhancement, Motion models, Deconvolution, Cameras, Edge detection

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Proceedings Article | 29 August 2016 Paper

Global denoising for 3D MRI

Ao Feng, Jing Peng, Xi Wu

Proceedings Volume 10033, 100331Q (2016) https://doi.org/10.1117/12.2243969

KEYWORDS: Denoising, 3D magnetic resonance imaging, Magnetic resonance imaging, 3D image processing, Image filtering, Digital filtering, Image segmentation, Tissues, Diagnostics, Image restoration

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Proceedings Article | 29 August 2016 Paper

Single image super resolution of 3D MRI using local regression and intermodality priors

Jing Hu, Xi Wu, Jiliu Zhou

Proceedings Volume 10033, 100334C (2016) https://doi.org/10.1117/12.2243617

KEYWORDS: Super resolution, 3D magnetic resonance imaging, Magnetic resonance imaging, Lawrencium, Image resolution, Image processing, 3D image processing, Associative arrays, Image restoration, Medical imaging

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Proceedings Article | 14 September 2001 Paper

Biosensor of endotoxin and sepsis

Yang Shao, Xiang Wang, Xi Wu, Wei Gao, Qing-hua He, Shaoxi Cai

Proceedings Volume 4414, (2001) https://doi.org/10.1117/12.440162

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Showing 5 of 6 publications

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