Smart Industrial Job Verification Allocation System Using RFID and Image Processing

This paper presents the design, development, and performance evaluation of a Smart Job Distribution and Quality Verification System built for small-to-medium manufacturing environments. The system merges RFID-based operator authentication, microcontroller-driven conveyor control, real-time ultrasonic detection, USB camera-based image acquisition, OpenCV classical inspection, and YOLOv5 deep-learning defect detection into a single, cohesive platform. A private dataset named Job QC Dataset, comprising 640×480 JPEG images annotated with Label Image and split 70/15/15 for training, validation, and testing, was used to train the YOLO model on Google Colab. Performance metrics including precision, recall, F1 score, and confusion matrix are reported. The system achieved a mean Average Precision (mAP@0.5) of 91.3%, with a precision of 0.934, recall of 0.887, and an F1 score of 0.910 on the test partition. These results confirm the viability of the proposed hybrid inspection framework for industrial deployment.

@article{ijter2026212606223228,
  author = {Miss. Kiran Ravindra Manole. and Dr.Saurabh R. Prasad. and Dr. Shrinivas A. Patil},
  title = {Smart Industrial Job Verification Allocation System Using RFID and Image Processing},
  journal = {International Journal of Technology &  Emerging Research },
  year = {2026},
  volume = {2},
  number = {6},
  pages = {175-185},
  doi =  {10.64823/ijter.2606015},
  issn = {3068-109X},
  url = {https://www.ijter.org/article/212606223228/smart-industrial-job-verification-allocation-system-using-rfid-and-image-processing},
  abstract = {This paper presents the design, development, and performance evaluation of a Smart Job Distribution and Quality Verification System built for small-to-medium manufacturing environments. The system merges RFID-based operator authentication, microcontroller-driven conveyor control, real-time ultrasonic detection, USB camera-based image acquisition, OpenCV classical inspection, and YOLOv5 deep-learning defect detection into a single, cohesive platform. A private dataset named Job QC Dataset, comprising 640×480 JPEG images annotated with Label Image and split 70/15/15 for training, validation, and testing, was used to train the YOLO model on Google Colab. Performance metrics including precision, recall, F1 score, and confusion matrix are reported. The system achieved a mean Average Precision (mAP@0.5) of 91.3%, with a precision of 0.934, recall of 0.887, and an F1 score of 0.910 on the test partition. These results confirm the viability of the proposed hybrid inspection framework for industrial deployment.},
  keywords = {I. - RFID authentication, Cyber-Physical Systems, Smart Manufacturing, Machine Vision Inspection, YOLOv5, Arduino Automation, Industrial Quality Control, Industry 4.0, Job Tracking System.},
  month = {Jun},
}

Copyright © 2025 Authors retain the copyright of this article. This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.