TagLabel: RFID Based Orientation and Material Sensing for Automated Package Inspection

TL;DR

TagLabel leverages RFID technology and machine learning to quickly and accurately identify package contents and orientation, enhancing logistics security and automation without opening packages.

Contribution

This paper introduces TagLabel, a novel RFID-based system that determines package orientation and contents using low-cost passive tags and machine learning, improving inspection speed and accuracy.

Findings

Achieves over 80% accuracy in package orientation detection.

Effectively identifies contents using RSSI and phase analysis.

Works with standard RFID hardware and realistic RF environments.

Abstract

Modern logistics systems face increasing difficulty in identifying counterfeit products, fraudulent returns, and hazardous items concealed within packages, yet current package screening methods remain too slow, expensive, and impractical for widespread use. This paper presents TagLabel, an RFID based system that determines both the orientation and contents of packages using low cost passive UHF tags. By analyzing how materials change RSSI and phase, the system identifies the contents of a package without opening it. Using orientation inferred from phase differences, tag occlusion, and antenna gain patterns, the system selects the tag with the greatest occlusion for accurate material sensing. We evaluate two and three tag configurations, and show that both can deliver high orientation and material sensing performance through the use of machine learning classifiers, even in realistic RF environments. When combined into a unified pipeline, TagLabel achieves more than 80 percent accuracy across all package orientations. Because it requires only standard RFID hardware and offers fast scanning times, this approach provides a practical way to enhance package inspection and improve automation in logistics operations.