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: YOLOv5, Industry 4.0, I. - RFID authentication, Cyber-Physical Systems, Smart Manufacturing, Machine Vision Inspection, Arduino Automation, Industrial Quality Control, Job Tracking System.