基于改进SinGAN的电力线巡检异物数据增强技术

针对电力线异物识别模型能使用的数据集较少,并且传统单幅自然图像的生成式模型(SinGAN)模型生成数据与异物识别模型匹配度不高、质量不佳、耗时过久的问题,提出了改进SinGAN模型。在改进SinGAN模型基础上加入仿射变换单元、大小变换单元进一步增强数据集,加入图像滤波单元提高电力线异物识别模型所需数据质量。并通过改进SinGAN反向传播训练过程和SinGAN的单精度生成器结构提升模型生成质量,减少所用时长。实验结果表明,经50次实验后,改进SinGAN的平均弗雷谢特起始距离(Fréchet inception distance, FID)为91.375,平均训练时长1.21 h。分别比传统SinGAN降低了27.247%和87.31%。改进SinGAN与其他主流生成式对抗网络相比有更好的异物数据生成能力,可以增强电力线异物识别模型所需数据,具有优越性。

Data enhancement technology of power line inspection foreign object based on improved SinGAN

Due to the attenuation and scattering of light in the water, the image captured underwater has the problems of color deviation, low contrast, poor definition and uneven illumination. In this paper, an underwater image enhancement method based on color correction and improved 2D gamma function is proposed. Firstly, MSRCR is used to correct the color deviation problem to obtain an input image; secondly, the improved two dimensional gamma function is used to reduce the influence of uneven illumination on the underwater image, and BEASF is used to enhance the image contrast to obtain another input image; finally, the four weights of contrast, saliency, saturation and exposure are combined for multi weight fusion to obtain the final enhancement image. Experimental results show that this algorithm can effectively improve the problem of underwater image color deviation, and enhance the details and contrast of the image.