Corrosion Detection Of Power Equipment Based On Convolution Neural Network

Metal corrosion is harmful to the power equipment,which is necessary to find and handle corrosion in time to avoid potential safety hazards.Corrosion detection of power equipment using robots instead of engineers can reduce the management cost and avert human negligence,and realize the real-time monitoring of power equipment.The robots collect equipment information according to the inspection path and send it to corrosion detection system for detection.Therefore,this method depends heavily on the corrosion detection ability deploying on edge equipment.Compared with other corrosion detection methods,the method based on convolutional neural network without artificially defined features,and become a research hotspot at present.However,the existing research on corrosion detection of power equipment only migrates original object detection models or improves the feature extraction networks,but ignores the difference between corrosion samples and common image samples.In this thesis,hierarchical annotation method is proposed for corrosion features;then,for the weak global information expression ability of feature map,self-attention mechanism is integrated into feature extraction network;finally,considering the limited computing resources of edge devices,PQF(Permute,Quantize and Fine-tune)method is adopted to compress the detection model to easier deploy it on edge devices.To sum up,this thesis proposes a corrosion detection system of power equipment based on convolutional neural network.The specific research contents include:(1)The irregularity and detachability of metal corrosion makes annotators confront ambiguity and uncertainty in the labeling process.This thesis proposes a novel hierarchical annotation method.This method can easily produce unified annotation results without ambiguity.And it highlights the corrosion features and increases the number of ground truth,realizing data augmentation.(2)Convolutional neural network expands its receptive field by stacking layers,but compared with attention mechanism,the global information expression ability is still insufficient.Therefore,behind the feature extraction network,self-attention mechanism is added to improve the global information expression ability of feature map.Experiments on large-scale image dataset Image Net show that this method improves the global information contained in feature map,then improves the classification ability of model.Further experiments show that merging self-attention further improves the performance of corrosion detection model.(3)Considering the large volume of original detection model,it is an optimizing direction to deploy model on edge devices after compression.The PQF method compresses network model by three steps: permute,quantize and fine-tune.Experiments show that this method greatly reduces the demand for computing resources of detection model when the performance decreases slightly.Therefore,after adopting PQF,more complex corrosion detection model becomes convenient to deploy it on edge equipment with limited performance and space.