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Exploring Image Generation for UAV Change Detection
Xuan Li; Haibin Duan; Yonglin Tian; Fei-Yue Wang
Source PublicationIEEE/CAA Journal of Automatica Sinica
AbstractChange detection (CD) is becoming indispensable for unmanned aerial vehicles (UAVs), especially in the domain of water landing, rescue and search. However, even the most advanced models require large amounts of data for model training and testing. Therefore, sufficient labeled images with different imaging conditions are needed. Inspired by computer graphics, we present a cloning method to simulate inland-water scene and collect an auto-labeled simulated dataset. The simulated dataset consists of six challenges to test the effects of dynamic background, weather, and noise on change detection models. Then, we propose an image translation framework that translates simulated images to synthetic images. This framework uses shared parameters (encoder and generator) and 22 × 22 receptive fields (discriminator) to generate realistic synthetic images as model training sets. The experimental results indicate that: 1) different imaging challenges affect the performance of change detection models; 2) compared with simulated images, synthetic images can effectively improve the accuracy of supervised models.
KeywordChange detection computer graphics image translation simulated images synthetic images unmanned aerial vehicles (UAVs)
WOS IDWOS:000804669600013
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Document Type期刊论文
Collection学术期刊_IEEE/CAA Journal of Automatica Sinica
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GB/T 7714
Xuan Li,Haibin Duan,Yonglin Tian,et al. Exploring Image Generation for UAV Change Detection[J]. IEEE/CAA Journal of Automatica Sinica,2022,9(6):1061-1072.
APA Xuan Li,Haibin Duan,Yonglin Tian,&Fei-Yue Wang.(2022).Exploring Image Generation for UAV Change Detection.IEEE/CAA Journal of Automatica Sinica,9(6),1061-1072.
MLA Xuan Li,et al."Exploring Image Generation for UAV Change Detection".IEEE/CAA Journal of Automatica Sinica 9.6(2022):1061-1072.
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