Autonomous Vision-Based Primary Distribution Systems Porcelain Insulators Inspection Using UAVs.
Autonomous Vision-Based Primary Distribution Systems Porcelain Insulators Inspection Using UAVs.
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DOI：
10.3390/s21030974
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发表时间：
2021-02-02
期刊：
影响因子：
--
通讯作者：
Jobaer S
中科院分区：
文献类型：
--
作者：
Rahman EU;Zhang Y;Ahmad S;Ahmad HI;Jobaer S
早期检测主配电系统中受损（部分破损）的户外绝缘子对于持续供电和公共安全至关重要。无人机（UAV）提供了一种更安全、自主和高效的方式来检查电力系统组件，而无需关闭配电系统。在这项工作中，一个新的数据集的设计，通过捕捉真实的图像使用无人机和手动生成的图像收集，以克服数据不足的问题。实现了一种基于深度拉普拉斯算子的超分辨率网络来重建高分辨率训练图像。为了提高弱光图像的可见性，弱光图像增强技术被用于训练图像的鲁棒曝光校正。一种不同的微调策略被实现用于微调对象检测模型，以提高特定故障绝缘子的检测精度。提出了几种飞行路径策略来克服绝缘体的遮蔽效应，沿着提供用于捕获电力系统部件的视频流的较不复杂且时间和能量有效的方法。不同的对象检测模型的性能，选择最合适的一个微调特定的故障绝缘子数据集。对于受损绝缘子的检测，我们提出的方法在两个不同的数据集上实现了0.81和0.77的F1分数，并提出了一种简单且更有效的飞行策略。我们的方法是基于真实的空中检查在役瓷绝缘子的几个视频序列显示强大的故障识别和诊断能力的广泛评估。我们的方法是证明了在斯瓦特，巴基斯坦的无人驾驶飞机获得的数据。
The early detection of damaged (partially broken) outdoor insulators in primary distribution systems is of paramount importance for continuous electricity supply and public safety. Unmanned aerial vehicles (UAVs) present a safer, autonomous, and efficient way to examine the power system components without closing the power distribution system. In this work, a novel dataset is designed by capturing real images using UAVs and manually generated images collected to overcome the data insufficiency problem. A deep Laplacian pyramid-based super-resolution network is implemented to reconstruct high-resolution training images. To improve the visibility of low-light images, a low-light image enhancement technique is used for the robust exposure correction of the training images. A different fine-tuning strategy is implemented for fine-tuning the object detection model to increase detection accuracy for the specific faulty insulators. Several flight path strategies are proposed to overcome the shuttering effect of insulators, along with providing a less complex and time- and energy-efficient approach for capturing a video stream of the power system components. The performance of different object detection models is presented for selecting the most suitable one for fine-tuning on the specific faulty insulator dataset. For the detection of damaged insulators, our proposed method achieved an F1-score of 0.81 and 0.77 on two different datasets and presents a simple and more efficient flight strategy. Our approach is based on real aerial inspection of in-service porcelain insulators by extensive evaluation of several video sequences showing robust fault recognition and diagnostic capabilities. Our approach is demonstrated on data acquired by a drone in Swat, Pakistan.
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