顾晓东, 唐丹宏, 黄晓华. 基于深度学习的电网巡检图像缺陷检测与识别[J]. 电力系统保护与控制, 2021, 49(5): 91-97. DOI: 10.19783/j.cnki.pspc.200517
引用本文: 顾晓东, 唐丹宏, 黄晓华. 基于深度学习的电网巡检图像缺陷检测与识别[J]. 电力系统保护与控制, 2021, 49(5): 91-97. DOI: 10.19783/j.cnki.pspc.200517
GU Xiao-dong, TANG Dan-hong, HUANG Xiao-hua. Deep learning-based defect detection and recognition of a power grid inspection image[J]. Power System Protection and Control, 2021, 49(5): 91-97. DOI: 10.19783/j.cnki.pspc.200517
Citation: GU Xiao-dong, TANG Dan-hong, HUANG Xiao-hua. Deep learning-based defect detection and recognition of a power grid inspection image[J]. Power System Protection and Control, 2021, 49(5): 91-97. DOI: 10.19783/j.cnki.pspc.200517

基于深度学习的电网巡检图像缺陷检测与识别

Deep learning-based defect detection and recognition of a power grid inspection image

  • 摘要: 无人机巡检已成为保证电网稳定运行的重要手段。针对巡检图像的自动化判读,提出基于深度学习的电网多部件缺陷检测与识别方法。将小样本缺陷检测问题分解为目标检测和分类两步。针对多目标部件的检测,提出基于最小凸集的损失函数以及预测框选择方法,两者结合YOLOv3框架可以实现多种部件的精准定位。之后,单类分类器在高维特征空间中进行小样本学习,判断目标部件是否故障。测试图像来自220 kV安徽宣枣4883线的巡检图像。实验结果表明,该方法对常见的电网故障识别率高于96%,漏报率低于2%,表明该方法能有效地进行电网的多部件缺陷检测与识别。未来结合边缘计算加速处理,可以实现无人机的在轨巡检。

     

    Abstract: Unmanned Aerial Vehicle(UAV) inspection has become an important means to ensure the stable operation of a power grid. For intelligent processing of the inspection image, a deep learning-based multi-component inspection of the power grid is proposed. The problem of small sample defect detection is resolved in two stages: target detection and classification. For multi-target detection, a new loss function and prediction box selection based on the smallest convex set is proposed. These allow YOLOv3 to detect various target components accurately. After that, one-class classification is employed for small sample learning to estimate the state of the detected components in high-dimensional space. The test images are captured from the 220 kV power transmission line called the Anhui Xuanzao 4883 line. Experimental results show that the recognition rate is above 96% and the false negative rate is lower than 2% for common defects of a power grid. The method can effectively identify the defects of various components in the power grid. In the future, combined with edge computing to accelerate processing, UAV onboard inspection can be realized.

     

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