基于多层级深度神经网络的电力设备红外图像故障识别

Fault Recognition of Power Equipment Infrared Images Based on Multilayer Deep Neural Networks

电力设备的故障可能导致电力系统不稳定甚至解列,对电力安全和国民经济造成巨大损失,因此迅速且准确地识别这些故障至关重要。红外图像特征在捕捉发热故障的电力设备方面表现出良好的特征表达能力。然而,在图像采集过程中,可能会发生目标重叠、遮挡以及类目标干扰等问题。因此提出了一种复杂图像故障识别算法。基于多层级深度神经网络,充分利用多层网络模块的高层次特征提取能力和多级网络模块的特征融合能力,以提高故障识别的准确性。实验结果表明,该算法在准确率和运行时间等评估指标上优于现有的Faster-RCNN、VGG16、VGG19以及传统Resnet等模型,验证了其在解决图像中目标重叠、遮挡和类目标干扰等问题上的有效性。

The malfunction of power equipment may lead to instability or even disconnection of the power sys-tem,causing huge losses to power safety and the national economy.It is crucial to quickly and accurately iden-tify these malfunctions.Infrared image features exhibit excellent feature expression ability in capturing the heating malfunctions of power equipment.However,during the image acquisition process,problems such as object overlap,occlusion and interference between categories may occur.Therefore,a complex image fault recognition algorithm based on multilayer deep neural networks is proposed in this paper,fully utilizing the high-level feature extraction capability of multilayer network modules and the feature fusion capability of multi-level network modules to improve the accuracy of fault recognition.Experimental results show that the pro-posed algorithm is superior to existing models such as Faster-RCNN,VGG16,VGG19 and traditional Resnet models in terms of accuracy and running time,and its effectiveness in solving problems such as target overlap,occlusion and class interference in images is verified.