Syn2Real Detection in the Sky: Generation and Adaptation of Synthetic Aerial Ship Images

Wu, Yaoyuan; Guo, Weijie; Tan, Zhuoyue; Zhao, Yifei; Zhu, Quanxing; Wu, Liaoni; Guo, Zhiming

doi:10.3390/app14114558

Open AccessArticle

Syn2Real Detection in the Sky: Generation and Adaptation of Synthetic Aerial Ship Images

by

Yaoyuan Wu

¹,

Weijie Guo

¹

,

Zhuoyue Tan

¹,

Yifei Zhao

²,

Quanxing Zhu

²,

Liaoni Wu

³

and

Zhiming Guo

^3,*

¹

Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China

²

College of The Environment & Ecology, Xiamen University, Xiamen 361102, China

³

School of Aerospace Engineering, Xiamen University, Xiamen 361102, China

^*

Author to whom correspondence should be addressed.

Appl. Sci. 2024, 14(11), 4558; https://doi.org/10.3390/app14114558

Submission received: 20 April 2024 / Revised: 23 May 2024 / Accepted: 24 May 2024 / Published: 25 May 2024

(This article belongs to the Special Issue AI-Based Image Processing: 2nd Edition)

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Abstract

Object detection in computer vision requires a sufficient amount of training data to produce an accurate and general model. However, aerial images are difficult to acquire, so the collection of aerial image datasets is a priority issue. Building on the existing research on image generation, the goal of this work is to create synthetic aerial image datasets that can be used to solve the problem of insufficient data. We generated three independent datasets for ship detection using engine and generative model. These synthetic datasets are rich in virtual scenes, ship categories, weather conditions, and other features. Moreover, we implemented domain-adaptive algorithms to address the issue of domain shift from synthetic data to real data. To investigate the application of synthetic datasets, we validated the synthetic data using six different object detection algorithms and three existing real-world, ship detection datasets. The experimental results demonstrate that the methods for generating synthetic aerial image datasets can complete the insufficient data in aerial remote sensing. Additionally, domain-adaptive algorithms could further mitigate the discrepancy from synthetic data to real data, highlighting the potential and value of synthetic data in aerial image recognition and comprehension tasks in the real world.

Keywords: Syn2Real; synthetic dataset; ship detection; aerial images

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MDPI and ACS Style

Wu, Y.; Guo, W.; Tan, Z.; Zhao, Y.; Zhu, Q.; Wu, L.; Guo, Z. Syn2Real Detection in the Sky: Generation and Adaptation of Synthetic Aerial Ship Images. Appl. Sci. 2024, 14, 4558. https://doi.org/10.3390/app14114558

AMA Style

Wu Y, Guo W, Tan Z, Zhao Y, Zhu Q, Wu L, Guo Z. Syn2Real Detection in the Sky: Generation and Adaptation of Synthetic Aerial Ship Images. Applied Sciences. 2024; 14(11):4558. https://doi.org/10.3390/app14114558

Chicago/Turabian Style

Wu, Yaoyuan, Weijie Guo, Zhuoyue Tan, Yifei Zhao, Quanxing Zhu, Liaoni Wu, and Zhiming Guo. 2024. "Syn2Real Detection in the Sky: Generation and Adaptation of Synthetic Aerial Ship Images" Applied Sciences 14, no. 11: 4558. https://doi.org/10.3390/app14114558

APA Style

Wu, Y., Guo, W., Tan, Z., Zhao, Y., Zhu, Q., Wu, L., & Guo, Z. (2024). Syn2Real Detection in the Sky: Generation and Adaptation of Synthetic Aerial Ship Images. Applied Sciences, 14(11), 4558. https://doi.org/10.3390/app14114558

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Syn2Real Detection in the Sky: Generation and Adaptation of Synthetic Aerial Ship Images

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