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Your search keyword '"Meng, Lingxuan"' showing total 33 results
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33 results on '"Meng, Lingxuan"'

1. Learning to Extract Building Footprints from Off-Nadir Aerial Images

Author

Wang, Jinwang, Meng, Lingxuan, Li, Weijia, Yang, Wen, Yu, Lei, and Xia, Gui-Song

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Extracting building footprints from aerial images is essential for precise urban mapping with photogrammetric computer vision technologies. Existing approaches mainly assume that the roof and footprint of a building are well overlapped, which may not hold in off-nadir aerial images as there is often a big offset between them. In this paper, we propose an offset vector learning scheme, which turns the building footprint extraction problem in off-nadir images into an instance-level joint prediction problem of the building roof and its corresponding "roof to footprint" offset vector. Thus the footprint can be estimated by translating the predicted roof mask according to the predicted offset vector. We further propose a simple but effective feature-level offset augmentation module, which can significantly refine the offset vector prediction by introducing little extra cost. Moreover, a new dataset, Buildings in Off-Nadir Aerial Images (BONAI), is created and released in this paper. It contains 268,958 building instances across 3,300 aerial images with fully annotated instance-level roof, footprint, and corresponding offset vector for each building. Experiments on the BONAI dataset demonstrate that our method achieves the state-of-the-art, outperforming other competitors by 3.37 to 7.39 points in F1-score. The codes, datasets, and trained models are available at https://github.com/jwwangchn/BONAI.git.

Published
2022

9. Design of a High-Frame-Rate and Large-Grayscale Simulation Projection System Based on Digital Micromirror Devices.

Author

Meng, Lingxuan, Yang, Yongqiang, Li, Hongyu, Tang, Yanfu, Li, Zhongming, Qu, Ying, Zhao, Min, and Li, Junlin

Subjects
DIGITAL technology, IMAGE sensors, MICROMIRROR devices, LIGHT intensity, GRAYSCALE model
Abstract

With the increasing acquisition speed of image sensors, it has become necessary to provide image sources with higher frame rates and grayscale in order to meet testing requirements. In the field of semi-physical simulation projection, digital micromirror devices are often chosen for their high resolution, uniformity, response speed, and energy concentration. In this study, we utilized digital micromirror devices to construct a high-frame-rate and large-grayscale simulation projection system. To achieve our experiment goals, we employed two digital micromirror devices. One DMD was used to modulate the light intensity of the light source, while the other generated images with different bit planes. By projecting the target images onto the image sensor, we were able to achieve a frame rate of 1611 hz for the projected 12-bit image. This system meets the requirements for our experiment design and ensures the accurate testing of image sensors. [ABSTRACT FROM AUTHOR]

Published
2024
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28. Real-Time Detection of Ground Objects Based on Unmanned Aerial Vehicle Remote Sensing with Deep Learning: Application in Excavator Detection for Pipeline Safety

Author

Ji Zhou, Andreas Baumann, Meng Lingxuan, Lu Zhenyu, Zhixing Peng, Yan Du, and Jirong Zhang

Subjects
010504 meteorology & atmospheric sciences, Workstation, Computer science, Science, Firefighting, ComputerApplications_COMPUTERSINOTHERSYSTEMS, 02 engineering and technology, ComputingMethodologies_ARTIFICIALINTELLIGENCE, 01 natural sciences, law.invention, remote sensing, law, 0202 electrical engineering, electronic engineering, information engineering, ComputerSystemsOrganization_SPECIAL-PURPOSEANDAPPLICATION-BASEDSYSTEMS, 0105 earth and related environmental sciences, Remote sensing, business.industry, Deep learning, Process (computing), deep learning, Object (computer science), Pipeline (software), real-time detection, Object detection, Excavator, YOLOv3, unmanned aerial vehicle (UAV), General Earth and Planetary Sciences, 020201 artificial intelligence & image processing, Artificial intelligence, unmanned aerial vehicle (uav), business, yolov3
Abstract

Unmanned aerial vehicle (UAV) remote sensing and deep learning provide a practical approach to object detection. However, most of the current approaches for processing UAV remote-sensing data cannot carry out object detection in real time for emergencies, such as firefighting. This study proposes a new approach for integrating UAV remote sensing and deep learning for the real-time detection of ground objects. Excavators, which usually threaten pipeline safety, are selected as the target object. A widely used deep-learning algorithm, namely You Only Look Once V3, is first used to train the excavator detection model on a workstation and then deployed on an embedded board that is carried by a UAV. The recall rate of the trained excavator detection model is 99.4%, demonstrating that the trained model has a very high accuracy. Then, the UAV for an excavator detection system (UAV-ED) is further constructed for operational application. UAV-ED is composed of a UAV Control Module, a UAV Module, and a Warning Module. A UAV experiment with different scenarios was conducted to evaluate the performance of the UAV-ED. The whole process from the UAV observation of an excavator to the Warning Module (350 km away from the testing area) receiving the detection results only lasted about 1.15 s. Thus, the UAV-ED system has good performance and would benefit the management of pipeline safety., Remote Sensing, 12 (1), ISSN:2072-4292

Published
2020

33. A Practical Cross-View Image Matching Method between UAV and Satellite for UAV-Based Geo-Localization.

Author

Ding, Lirong, Zhou, Ji, Meng, Lingxuan, and Long, Zhiyong

Subjects
IMAGE registration, DRONE aircraft, NAVIGATION, REMOTE-sensing images
Abstract

Cross-view image matching has attracted extensive attention due to its huge potential applications, such as localization and navigation. Unmanned aerial vehicle (UAV) technology has been developed rapidly in recent years, and people have more opportunities to obtain and use UAV-view images than ever before. However, the algorithms of cross-view image matching between the UAV view (oblique view) and the satellite view (vertical view) are still in their beginning stage, and the matching accuracy is expected to be further improved when applied in real situations. Within this context, in this study, we proposed a cross-view matching method based on location classification (hereinafter referred to LCM), in which the similarity between UAV and satellite views is considered, and we implemented the method with the newest UAV-based geo-localization dataset (University-1652). LCM is able to solve the imbalance of the input sample number between the satellite images and the UAV images. In the training stage, LCM can simplify the retrieval problem into a classification problem and consider the influence of the feature vector size on the matching accuracy. Compared with one study, LCM shows higher accuracies, and Recall@K (K ∈ {1, 5, 10}) and the average precision (AP) were improved by 5–10%. The expansion of satellite-view images and multiple queries proposed by the LCM are capable of improving the matching accuracy during the experiment. In addition, the influences of different feature sizes on the LCM's accuracy are determined, and we found that 512 is the optimal feature size. Finally, the LCM model trained based on synthetic UAV-view images was evaluated in real-world situations, and the evaluation result shows that it still has satisfactory matching accuracy. The LCM can realize the bidirectional matching between the UAV-view image and the satellite-view image and can contribute to two applications: (i) UAV-view image localization (i.e., predicting the geographic location of UAV-view images based on satellite-view images with geo-tags) and (ii) UAV navigation (i.e., driving the UAV to the region of interest in the satellite-view image based on the flight record). [ABSTRACT FROM AUTHOR]

Published
2021
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