Rapid retrieval system of UAV image based on deep learning

Yating Xue; Shengwei Wu; Jiangtao Wang; Jieping Chen

doi:10.1117/12.3007428

30 August 2023 Rapid retrieval system of UAV image based on deep learning

Yating Xue, Shengwei Wu, Jiangtao Wang, Jieping Chen

Proceedings Volume 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023); 127971A (2023) https://doi.org/10.1117/12.3007428
Event: 2nd International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 2023, Qingdao, China

Abstract

The rapid development of unmanned aerial vehicle (UAV) aerial survey technology and deep learning has revolutionized traditional aerial survey methods. With its fast measurement, low cost, good safety, and high reliability, UAVs can quickly obtain image data of a certain survey area in a relatively short time. Although UAV surveying has been used in various fields of related industries, there are still certain limitations to quickly obtaining desired information from a group of images. Therefore, this paper combines the VGG16 deep learning model to match UAV images and make it applicable to the web environment. Starting with extracting image features of UAVs from the VGG16 model, this paper implements a UAV image feature extraction and retrieval system based on VGG16. Then, the matching efficiency is compared with the matching model based on the SIFT algorithm. Finally, the image feature extraction and retrieval system based on VGG16 is combined with the web system to study the retrieval of image data of interested areas or image data without spatial information, as well as spatial information queries under web conditions.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yating Xue, Shengwei Wu, Jiangtao Wang, and Jieping Chen "Rapid retrieval system of UAV image based on deep learning", Proc. SPIE 12797, Second International Conference on Geographic Information and Remote Sensing Technology (GIRST 2023), 127971A (30 August 2023); https://doi.org/10.1117/12.3007428

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KEYWORDS

Image retrieval

Feature extraction

Unmanned aerial vehicles

Deep learning

Databases

Visualization

Image classification

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