Mr. Wenjun Lu Profile

Wenjun Lu

at Anhui Sanlian Univ

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Publications (2)

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Proceedings Article | 7 August 2024 Paper

Comprehensive calibration and test system for UAVs

Wenjun Lu

Proceedings Volume 13224, 132240B (2024) https://doi.org/10.1117/12.3034830

KEYWORDS: Calibration, Unmanned aerial vehicles, Control systems, Sensor calibration, Inspection equipment, Sensors, Prototyping

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To fulfill the requirements for unmanned aerial vehicle (UAV) equipment testing, usage, maintenance, and repair, and to achieve the goals of forward support, rapid support, and comprehensive support, a comprehensive calibration and testing system for UAVs has been designed and implemented. This system focuses on calibrating and testing multiple subsystems including the wireless data link, flight control system, onboard sensors, power This system focuses on calibrating and testing multiple subsystems including the wireless data link, flight control system, onboard sensors, power system, and overall wiring of the UAV. It incorporates various subsystem calibration and testing equipment to ensure accurate and reliable results. It incorporates various subsystem calibration and testing equipment to ensure accurate and reliable results. The system design is rational, utilizing scientific methods and employing advanced technology, resulting in stable and consistent performance. The system design is rational, utilizing scientific methods and employing advanced technology, resulting in stable and consistent performance. The experimental outcomes affirm the effectiveness of system in meeting the calibration and testing requirements of UAVs.

Proceedings Article | 14 August 2019 Paper

Blind quality assessment of on-board reconnaissance images

Wenjun Lu, Congli Li, Song Xue

Proceedings Volume 11179, 111790U (2019) https://doi.org/10.1117/12.2539695

KEYWORDS: Image quality, Reconnaissance, RGB color model, Feature extraction, Cameras, Image processing, Statistical analysis, Image compression, Roentgenium, Algorithm development

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Due to imaging platform and conditions constraints, multiple types of hybrid distortions exist in on-board reconnaissance images. Research on quality assessment of reconnaissance images can provide important quantitative basis and reference for performance optimization of subsequent processing and imaging system. By analyzing characteristics of reconnaissance images, 11 kinds of relevant features from 3 categories such as camera shake, structure changes, and color loss are extracted in conditions of multi-degree freedom and multi-attitude changes of imaging platform. Here we use high resolution mapping images as the original image set, and extract features of image patches. Benchmark distribution characteristics are obtained by multivariate Gaussian fitting. Using the learned multivariate Gaussian model, a Mahalanobis distance is used to measure the quality of each patch of on-board reconnaissance images, then overall quality score is obtained by average pooling. When tested images from real on-board imaging platform, the proposed method is shown to correlate highly with human judgments of quality and have superior quality-prediction performance to state-of-the-art blind image quality assessment methods.

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