Prof. Yulong Qi Profile

Prof. Yulong Qi

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Publications (4)

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Proceedings Article | 30 April 2024 Paper

3D tracer particle field reconstruction based on 3D CNN in SAPIV

Xiangju Qu, Peng Zhang, Rui Qu, Xiao Xiao, Yulong Qi, Xiang Liu, Chang Liu

Proceedings Volume 13156, 131561E (2024) https://doi.org/10.1117/12.3018841

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The three-dimensional particle image velocimetry (3D PIV) technique, as a non-intrusive method for three-dimensional full-field velocity measurement, has garnered extensive utilization across diverse domains including biomimetic dynamics, combustion diagnostics, and the structural design of aerospace equipment. Synthetic Aperture Particle Image Velocimetry (SAPIV), based on camera arrays, digitally merges images obtained from different perspectives to simulate the imaging effects of large-aperture cameras. This approach allows for large-scale, high-resolution flow field measurements. In this study, the three-dimensional intensity characteristics of particles within sequences of refocused images are investigated. Leveraging the spatial distribution patterns of grayscale information for individual focused particles, we designed a three-dimensional convolutional neural network (3DCNN) capable of extracting focused particle positions. Throughout the particle extraction procedure, this three-dimensional CNN network systematically analyzes the sequence of refocused images and subsequently derives both particle positions and grayscale information for focused particles based on their distinctive characteristics. The tracer particle field in simulated experiments were reconstructed and the reconstruction quality was evaluated. The results demonstrate the high precision of our proposed method in reconstructing three-dimensional tracer particle information in SAPIV.

Proceedings Article | 30 April 2024 Paper

Spray droplet field reconstruction based on synthetic aperture imaging

Xiangju Qu, Hao Liu, Rui Qu, Yulong Qi, Chang Liu, Xiang Liu

Proceedings Volume 13156, 1315611 (2024) https://doi.org/10.1117/12.3017960

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Quantitative analysis of spray droplet fields plays a pivotal role in various domains, encompassing internal combustion engine combustion diagnostics, equipment spray coating and corrosion prevention, and unmanned aerial vehicle-based agricultural pesticide dispersion. Precise measurement of the spatial distribution of spray droplet fields facilitates accurate control and orientation of spraying, thereby propelling the intelligent evolution of both industrial and agricultural sectors. In light of the substantial dimensions of spray fields, achieving focused imaging of all droplets on the camera imaging plane during reconstruction proves unattainable. Addressing this challenge, this study suggests employing a four-camera array configuration. According to the characteristics of the defocusing blur of spray droplets, the cameras on the array capture images of the droplets from diverse perspectives. Subsequently, these images are merged through a refocusing process. This method offers accurate extraction of out-of-focus droplet centers. Employing three-dimensional cross-correlation analysis, the motion trajectories of the spray droplet field can be inferred with precision.

Proceedings Article | 23 January 2023 Paper

Grain loading condition monitoring based on dual cameras in intelligent cluster grain truck

Xiangju Qu, Zhiqiang Hu, Yulong Qi, Miaomiao Wang, Chaowen Lin, Xiang Liu

Proceedings Volume 12557, 125571M (2023) https://doi.org/10.1117/12.2651750

KEYWORDS: Cameras, Imaging systems, Agriculture, Calibration, Machine vision, Control systems, Computer vision technology, CMOS cameras

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Intelligent harvesting is one of the important criteria to measure the development level of agricultural modernization. Coordinated operation of harvester-grain truck clusters can improve grain harvesting efficiency and reduce post-production losses during large-scale rice/wheat concentrated harvests. Overloading the grain on the grain truck will cause serious scattering of grain, further, insufficient loading can result in wasted capacity. How to monitor the grain loading process dynamically is a pressing matter. In this paper, two cameras and a point laser were used to measure the status of grains in the truck in real-time. The loadable capacity of the grain truck can be obtained through the reconstruction of the grain truck carriage edge and the positioning of the bottom of the truck carriage. During the harvester grain unloading process, the cone tip of the wheat pile is irradiated by the laser, and the height of the wheat pile can be obtained by measuring the location of the laser point. Then insufficient loading or overloading can be avoided by controlling the speed of the unloading port. This method has been verified in the paper box. The results show that the dual-camera monitoring system can measure the volume of the grain truck in real-time, and feedback on the total amount of grain loaded in the grain truck in time, which can effectively avoid grain loss caused by excessive loading.

Proceedings Article | 19 December 2022 Poster + Paper

3D observation of grain pile based on projection light and dual cameras

Yuanyue Zhang, Yulong Qi, Xiangju Qu, Wenhao Fan, Chang Liu

Proceedings Volume 12319, 123191J (2022) https://doi.org/10.1117/12.2643753

KEYWORDS: Cameras, Bandpass filters, Optical filters, Imaging systems, Calibration, 3D metrology

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We adopt Unreal Engine to construct a virtual football stadium. In the virtual football stadium, we set two teams of AI players controlled by behavior trees, and simulate football games by playing against each other. At the same time, we deploy a virtual camera in the auditorium that can be rotated horizontally and vertically to capture the picture. We adopt the Transmission Control Protocol communication plugin in the mall to realize the communication between Unreal Engine and programs. Saving the current frame picture locally through the built-in screenshot command of Unreal Engine, and send the internal game information to be read to the client at the same time. The client reads the local image, accepts the information and sends the corresponding action to the Unreal Engine. Unreal Engine executes the received action to rotate the camera.

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