Abstract
Unmanned aerial vehicle (UAV) patrol inspection is an important means of transmission line detection. However, during UAV patrol, due to the UAV or the bad weather, there may be droplets on the camera lens, which will especially reduce the effect of small target detection. In this paper, a data enhancement method based on improved generative adversarial network (GAN) is proposed for automatically removing droplets from UAV images of transmission lines. In order to take more account of the context information of small targets when removing droplets, this method integrates the attention mechanism in the design of generator. Experiment shows that compared with the benchmark method, the proposed method can make the image after removing droplets more similar to the original image, and increase the structural similarity (SSIM) by 8.69%. The practical value of this method is further proved in the process of object detection of droplets removed images.
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Acknowledgement
This work was supported in part by the Key R &D Project of Shandong Province under Grant No. 2022CXGC010503, the Youth Foundation of Shandong Province under Grant No. ZR202102230323, the National Natural Science Foundation for Young Scientists of China under Grant No. 61903155, and the Doctoral Scientific Fund Project under Grant No. xbs1910.
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Wang, W., Huang, W., Zhao, H., Zhang, M., Qiao, J., Zhang, Y. (2022). Data Enhancement Method Based on Generative Adversarial Network for Small Transmission Line Detection. In: Zhang, H., et al. Neural Computing for Advanced Applications. NCAA 2022. Communications in Computer and Information Science, vol 1638. Springer, Singapore. https://doi.org/10.1007/978-981-19-6135-9_31
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DOI: https://doi.org/10.1007/978-981-19-6135-9_31
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