As the failure of power line insulators leads to the failure of power transmission systems, an insulator inspection system based on an aerial platform is widely used. Insulator defect detection is performed against complex backgrounds in aerial images, presenting an interesting but challenging problem. Traditional methods, based on handcrafted features or shallow-learning techniques, can only localize insulators and detect faults under specific detection conditions, such as when sufficient prior knowledge is available, with low background interference, at certain object scales, or under specific illumination conditions. This paper discusses the automatic detection of insulator defects using aerial images, accurately localizing insulator defects appearing in input images captured from real inspection environments. We propose a novel deep convolutional neural network (CNN) cascading architecture for performing localization and detecting defects in insulators. The cascading network uses a CNN based on a region proposal network to transform defect inspection into a two-level object detection problem. To address the scarcity of defect images in a real inspection environment, a data augmentation method is also proposed that includes four operations: 1) affine transformation; 2) insulator segmentation and background fusion; 3) Gaussian blur; and 4) brightness transformation. Defect detection precision and recall of the proposed method are 0.91 and 0.96 using a standard insulator dataset, and insulator defects under various conditions can be successfully detected. Experimental results demonstrate that this method meets the robustness and accuracy requirements for insulator defect detection.

中文翻译：

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使用卷积神经网络分析的航空图像检测电力线绝缘体缺陷

由于电力线路绝缘子故障导致输电系统故障，基于高空作业平台的绝缘子检测系统得到广泛应用。绝缘体缺陷检测是针对航拍图像中的复杂背景进行的，这是一个有趣但具有挑战性的问题。基于手工特征或浅层学习技术的传统方法只能在特定检测条件下定位绝缘体并检测故障，例如当有足够的先验知识、背景干扰低、在某些物体尺度下或在特定照明条件下。本文讨论了使用航拍图像自动检测绝缘体缺陷，准确定位出现在从真实检测环境捕获的输入图像中的绝缘体缺陷。我们提出了一种新颖的深度卷积神经网络 (CNN) 级联架构，用于执行定位和检测绝缘体中的缺陷。级联网络使用基于区域提议网络的 CNN 将缺陷检测转换为两级对象检测问题。为了解决真实检测环境中缺陷图像的稀缺问题，还提出了一种数据增强方法，包括四个操作：1）仿射变换；2）绝缘体分割和背景融合；3) 高斯模糊；4）亮度变换。使用标准绝缘子数据集，该方法的缺陷检测精度和召回率分别为0.91和0.96，可以成功检测各种条件下的绝缘子缺陷。