Research on Electric Meter Damage Detection Based on Deep Attention Re-sidual Network
Currently,manual detection is the mainstream method for detecting the appearance damage of disassembled electric energy meters,which seriously restricts the recognition effi-ciency of disassembled electric energy meters.Therefore,a deep convolutional neural network model RAN based on attention mechanism and residual idea is proposed.According to the atten-tion mechanism,channel and spatial attention modules are constructed in RAN to enhance the feature extraction ability of the network,and residual idea is used to avoid the feature value at-tenuation caused by the attention module.The research objects include fire gauges,water im-mersion gauges,terminal abnormality gauges,display screen damage gauges,appearance rup-ture gauges,and normal gauges.The average recognition rate of RAN is 94.58％,which is 0.32％ to 14.60％ higher than ResNet101,VGG16,MobileNet,and ShuffleNet.
disassembled electric energy meterAppearance damageAttention mechanismRe-sidual neural network
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