受拍摄环境及局部放电程度影响, 夜间型紫外相机拍摄电晕放电图像不清晰、放电区域的颜色不仅接近背景颜色且与背景交叉重叠等导致难以自动分割局部放电, 针对该问题提出一种新的电力线紫外图像局部放电区域精确分割方法。首先, 构建基于 Unet深度学习语义分割模型, 利用已训练 Unet网络对紫外图像语义分割获得电晕放电区域粗分割结果; 其次, 将放电区域紫外图像转换为灰度图像, 基于前景加权的 Otsu阈值分割法对粗分割结果进行精确分割。对 426个样本进行测试, 本文方法全部分割出了样本图像中的局部放电区域, 且分割出的放电区域与真值之间的误差接近 0, 所提出的电晕放电分割方法能为局部放电大小量化和评估提供准确的数据源。

Corona discharge images collected with night-type ultraviolet cameras are affected by the photographer’s environment and the degree of partial discharge, and the color of the discharge area is not only close to the background but also overlaps with the background, which makes it difficult to automatically segment corona discharge. This paper proposes a coarse-to-fine corona discharge ultraviolet (UV) image segmentation method. First, a deep-learning semantic segmentation model was constructed, and rough segmentation results of the corona discharge were obtained using a trained Unet network. Second, the UV image of the discharge region was converted into a gray image, and the rough segmentation result was accurately segmented based on the Otsu threshold segmentation method with foreground weighting. A total of 426 samples were tested, and all the corona discharge regions in the sample images were segmented using the proposed method. The error between the segmented discharge regions and the true value was close to 0. The proposed corona discharge segmentation method provides accurate data sources for the quantification and evaluation of corona discharges.