To increase the efficiency of targeted deployment and fault handling in electric power wireless mesh networks,a power terminal device recognition system is designed based on the inte-gration of deep learning and super-resolution reconstruction technique.To solve the problem of missed detections in identifying large-scale remote sensing images,the overlap segmentation method and the super-resolution reconstruction technique are adopted.The SPD-Conv module,which is the combination of the SPD layer and non-Stride convolution layer,is included to optimize the YOLOv5 algorithm,so as to enhance the effectiveness of small object detection,and train it on the constructed dataset of transmission tower images from remote sensing satellite(TIRS).Experiment results dem-onstrate that the system achieves a recognition speed of millisecond level for individual devices,with a mean average precision(mAP)of 0.833,which can effectively increase the efficiency of power ter-minal device detection.
remote sensing imageswireless private networkdeep learningimage recognitiondata-set of transmission tower images from remote sensing
NETL
NSTL
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