The strain clamps and leading wires are important components that connect conductors on overhead transmission lines and conduct current. During operation, poor contact between these components can cause abnormal overheating, leading to electric failures and threatening power system reliability. Recently, the use of unmanned aerial vehicles equipped with infrared thermal imagers for strain clamp and leading wire maintenance has become increasingly popular. Deep learning-based image recognition shows promising prospects for intelligent fault diagnosis of overheating faults. A pre-treatment method is proposed based on dynamic histogram equalisation to enhance the contrast of infrared images. The DeepLab v3+ network, loss function, and existing networks with different backbones are compared. The DeepLab v3+ network with ResNet101 and convolutional block attention module added, and the Focal Loss function achieved the highest performance with an average pixel accuracy of 0.614, an average intersection over union (AIoU) of 0.567, an F1 score of 0.644, and a frequency weighted intersection over union of 0.594 on the test set. The optimised Atrous rates has increased the AIoU by 12.91%. Moreover, an intelligent diagnosis scheme for evaluating the defect state of the strain clamps and leading wires is proposed and which achieves a diagnostic accuracy of 91.0%.

中文翻译：

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基于语义分割的架空输电线路智能过热故障诊断

耐张线夹和引出线是架空输电线路上连接导体并传导电流的重要部件。在运行过程中，这些部件之间的接触不良会导致异常过热，导致电力故障，威胁电力系统的可靠性。近年来，使用配备红外热像仪的无人机进行耐张钳和引线维护已变得越来越流行。基于深度学习的图像识别在过热故障智能故障诊断方面显示出广阔的前景。提出一种基于动态直方图均衡的预处理方法来增强红外图像的对比度。对 DeepLab v3+ 网络、损失函数以及具有不同主干网的现有网络进行了比较。添加了 ResNet101 和卷积块注意力模块的 DeepLab v3+ 网络，Focal Loss 函数取得了最高性能，平均像素精度为 0.614，平均交集（AIoU）为 0.567，F1 得分为 0.644，频率测试集上并集的加权交集为 0.594。优化后的 Atrous 速率使AIoU提高了12.91%。此外，提出了一种评估耐张线夹和引线缺陷状态的智能诊断方案，诊断准确率达到91.0%。