基于深度学习的电网巡检图像缺陷检测与识别

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。

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

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.