Automated Quality Control System for Canned Tuna Production using Artificial Vision

TL;DR

This paper introduces an automated quality control system for canned tuna using artificial vision, integrating industry 4.0 technologies and deep learning for real-time fault detection and classification.

Contribution

It presents a novel integrated system combining artificial vision, IoT, and deep learning models for efficient, autonomous quality control in tuna can production.

Findings

Real-time fault detection with YOLOv5

Enhanced resource optimization and product quality

System autonomy reduces operator workload

Abstract

This scientific article presents the implementation of an automated control system for detecting and classifying faults in tuna metal cans using artificial vision. The system utilizes a conveyor belt and a camera for visual recognition triggered by a photoelectric sensor. A robotic arm classifies the metal cans according to their condition. Industry 4.0 integration is achieved through an IoT system using Mosquitto, Node-RED, InfluxDB, and Grafana. The YOLOv5 model is employed to detect faults in the metal can lids and the positioning of the easy-open ring. Training with GPU on Google Colab enables OCR text detection on the labels. The results indicate efficient real-time problem identification, optimization of resources, and delivery of quality products. At the same time, the vision system contributes to autonomy in quality control tasks, freeing operators to perform other functions within the company.