Deep learning-based object detection is a powerful approach for detecting faulty insulators in power lines. This involves training an object detection model from scratch, or fine tuning a model that is pre-trained on benchmark computer vision datasets. This approach works well with a large number of insulator images, but can result in unreliable models in the low data regime. The current literature mainly focuses on detecting the presence or absence of insulator caps, which is a relatively easy detection task, and does not consider detection of finer faults such as flashed and broken disks. In this article, we formulate three object detection tasks for insulator and asset inspection from aerial images, focusing on incipient faults in disks. We curate a large reference dataset of insulator images that can be used to learn robust features for detecting healthy and faulty insulators. We study the advantage of using this dataset in the low target data regime by pre-training on the reference dataset followed by fine-tuning on the target dataset. The results suggest that object detection models can be used to detect faults in insulators at a much incipient stage, and that transfer learning adds value depending on the type of object detection model. We identify key factors that dictate performance in the low data-regime and outline potential approaches to improve the state-of-the-art.
翻译:深层学习天体探测是发现电线中故障绝缘器的强有力方法。 这涉及从零开始对物体探测模型进行培训,或对在基准计算机视觉数据集上预先培训的模型进行微调。 这个方法与大量绝缘机图像运作良好,但可以在低数据系统中产生不可靠的模型。 目前的文献主要侧重于发现绝缘机盖的存在或不存在,这是一个相对容易的探测任务,不考虑探测闪光和断裂磁盘等细微断层。 在文章中,我们从空中图像中为绝缘器和资产检查设计出三个对象探测任务,侧重于磁盘中的初始断层。 我们制作了一个庞大的绝缘机图像参考数据集,可用于学习稳健的特征,以探测健康和断层断层器。 我们研究在低目标数据系统中使用该数据集的好处,先对参考数据集进行培训,然后对目标数据集进行微调。 研究结果表明,可以使用对象探测模型来探测断层器中的断层断层器和资产检查,重点是磁盘中的定位器定位图案,在高端阶段,我们学习了低位状态的定位模型, 学习方向定位工具的定位工具,将提高方向定位工具的定位的定位,在低状态中学习方向定位的状态中, 学习方向定位工具的定位工具的定位工具的定位,在低状态中增加了方向定位的定位的定位的定位。