Rfid Dynamic Performance Measurement System Embedded In Multiscale Deep Learning

Multitag sensitivity would be affected by electromagnetic coupling during simultaneous reading. The reading distance of multitag depends on the least sensitivity and operating power of all tags, which is an indicator of reading performance. The main purpose of this article is to optimize the reading performance of multitag by embedding deep learning, while the reading distance of multitag changes with the 3-D geometric structure. Dynamic multitag image deblurring based on multiscale convolutional neural network (MCNN) improves image restoration ability and clarity. Also, tag detection from the estimated image via YOLOv3 improved by feature enhancement (YOLOv3_f) can improve the detection ability of small size targets, and mean average precision (mAP) is increased by 16.4%. Finally, the 3-D coordinates of tags in pixel space are converted into 3-D coordinates of world space by a quaternion. Comparing our system with the positioning method without deblurring, the 3-D coordinate structures are tested in the dynamic measurement system. The experimental results show that the reading performance of the designed RFID system has been greatly improved as the number of tags increases. Our scheme can improve the reading distance of multitag from the physical structure and the anticollision ability of the RFID system.