Abstract
With the continuous development of UAV technology, the application of UAV is more and more extensive. In aerial photography, reconnaissance, detection and autonomous driving navigation, the application of UAV is inseparable from the acquisition of clear images. However, in some complex weather conditions, such as foggy weather, windy and rainy weather, etc., the quality of the target image obtained by the UAV imaging system may be affected to a certain extent. In these processes, there will be some unavoidable external factors and hard conditions, which will reduce the quality of the image and make the information that people can obtain from the image become blurred. The data used in most UAV image system training is a data set composed of clear images. In order to solve the image acquisition of UAV in complex weather environment, this paper studies an image inpainting method based on Cycle GAN. This paper uses Jun-Yan Zhu’s open source Cycle GAN program to train a repair network model for complex weather images suitable for UAV use.
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References
Goodfellow, I., Pouget-Abadir, J., Mirza, M., et al.: Generative adversarial nets. In: Proceedings of the 27th International Conference on Neural Information Processing Systems, pp. 2672–2680. New York, USA (2014)
Li, B., Peng, X., Wang, Z., et al: All-in-one dehazing network. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 4770–4778 (2017)
Zhu, J.Y., Park, T., Isola, P., et al.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
Engin, D., Genç, A., Kemal Ekenel, H.: Cycle-dehaze: enhanced CycleGAN for single image dehazing. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, pp. 825–833(2018)
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. Mon, 24 Aug 2020 16:51:03 UTC
Deng, Z., Zhu, L., Hu, X., et al.: Deep multi-model fusion for single-image dehazing. In: Proceedings of IEEE International Conference on Computer Vision, pp. 2453–2462. Washington D.C., USA. IEEE (2019)
Redmon, J., Farhadi, A.: YOLO9000: better, faster, stronger. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7263–7271 (2017)
Acknowledgement
The author acknowledges funding received from the following science foundations: National Natural Science Foundation of China (No. 62176214, 61973253, 62101590), Natural Science Foundation of the Shaanxi Province, China (2021JQ-368).
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Weng, W., Huang, H., Du, Z., Zhang, L., Wang, J. (2023). A GAN-Based UAV Platform Complex Weather Image Restoration Technology. In: Fu, W., Gu, M., Niu, Y. (eds) Proceedings of 2022 International Conference on Autonomous Unmanned Systems (ICAUS 2022). ICAUS 2022. Lecture Notes in Electrical Engineering, vol 1010. Springer, Singapore. https://doi.org/10.1007/978-981-99-0479-2_208
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DOI: https://doi.org/10.1007/978-981-99-0479-2_208
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