Research on RFID electronic label detection based on YOLOv5
In the production process of Radio Frequency Identification(RFID)electronic labels,various defects such as ink dots,adhesive tape,and misalignment often occur randomly due to imperfect manufacturing techniques,operational errors,and equipment issues.To assist factories in reducing costs,improving efficiency,and accelerating the intelligent upgrade of automated production lines,this study employs the YOLOv5 deep learning algorithm in conjunction with visual detection devices to automati-cally identify defects in RFID electronic labels on the production line.The test results of the proposed RFID electronic label detec-tion method using the YOLOv5 model show an impressive accuracy of 98.9%in defect detection.Deploying the model on the pro-duction line demonstrates comprehensive coverage,high speed,and high accuracy in defect detection,significantly enhancing the efficiency of RFID electronic label inspection.
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