Deep learning technology has received extensive consideration in recent years, and its application value in target detection is also increasing day by day. In order to accelerate the practical process of deep learning technology in electric transmission line defect detection, the current work used the improved Faster R-CNN algorithm to achieve data-driven iterative training and defect detection functions for typical transmission line defect targets. Based on Faster R-CNN, we proposed an improved network that combines deformable convolution and feature pyramid modules and combined it with a data-driven iterative learning algorithm; it achieves extremely automated and intelligent transmission line defect target detection, forming an intelligent closed-loop image processing. The experimental results show that the increase of the recognition of improved Faster R-CNN network combined with data-driven iterative learning algorithm for the pin defect target is 31.7% more than Faster R-CNN. In the future, the proposed method can quickly improve the accuracy of transmission line defect target detection in a small sample and save manpower. It also provides some theoretical guidance for the practical work of transmission line defect target detection.

中文翻译：

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数据驱动的迭代学习算法在输电线路缺陷检测中的应用

近年来，深度学习技术受到了广泛的关注，其在目标检测中的应用价值也在日益提高。为了加快深度学习技术在输电线路缺陷检测中的实践过程，当前的工作是使用改进的Faster R-CNN算法为典型的输电线路缺陷目标实现数据驱动的迭代训练和缺陷检测功能。基于Faster R-CNN，我们提出了一种改进的网络，该网络结合了可变形卷积和特征金字塔模块，并将其与数据驱动的迭代学习算法相结合；它实现了高度自动化和智能的传输线缺陷目标检测，从而形成了智能的闭环图像处理。实验结果表明，改进的Faster R-CNN网络结合数据驱动的迭代学习算法对引脚缺陷目标的识别率比Faster R-CNN高31.7％。未来，该方法可以在小样本情况下快速提高输电线路缺陷目标检测的准确性，节省人力。这也为输电线路缺陷目标检测的实际工作提供了一些理论指导。