Abstract: Aiming at the problems of low detection accuracy and large reading error when the substation inspection robot conducts pointer instrument reading recognition outdoors,a pointer instrument reading recognition method based on CenterNet and DeepLabv3+ is proposed. ECA-Net is a local cross-channel interaction strategy without dimensionality reduction and adaptive selection of one-dimensional convolutional kernel size method.In the backbone network of CenterNet,ECA-Net lightweight attention mechanism module is introduced,which strengthens the characteristic connection between different channels. In the ASPP module of DeepLabv3+,the DAMM dual attention mechanism module is connected in parallel,and the positional attention module in the DAMM module can effectively simulate the long-term context-dependent information between image positions,connect different local feature information and improve the ability of semantic segmentation.The channel attention module in the DAMM module uses the correlation between the related category features of different channels to strengthen the characteristics of different categories and improve the accuracy of pixel classification. The elliptic perspective transformation and affine transformation based on linear transformation theory are used to correct the distortion of the instrument image,obtain the upright image of the instrument,improve the accuracy of pointer straight line fitting angle,and thus reduce the reading error. A large number of simulations and field tests were carried out using this method.The results showed that in the instrument detection stage,the mAP of the proposed model was increased by 7. 51% higher than the original model. In the stage of instrument reading recognition,the nominal error between the predicted value of the instrument reading and the true value of the instrument before correction is 6. 0%,and the average error is 4. 2%,and the nominal error between the predicted value of the instrument reading and the true value of the instrument is 2. 0% and the average error is 1. 3%,which verifies the effectiveness of the proposed method..