Automated Quality Control System for Canned Tuna Production using Artificial Vision

This work 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 a...

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Bibliographic Details
Published in2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT) pp. 1 - 6
Main Authors Gonza'Lez, Sendey Vera, Jimenez, Luis Chuquimarca, Given, Wilson Galdea Gonzalez, Noboa, Bremnen Veliz, Enderica, Carlos Saldana
Format Conference Proceeding
LanguageEnglish
Published IEEE 03.05.2024
Subjects
Artificial Vision
Convolutional Neural Networks
Graphics processing units
Metals
OCR Recognition
Optical character recognition
Text detection
Text recognition
Training
YOLO
YOLOv5
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DOI10.1109/AIIoT58432.2024.10574669

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Summary:This work 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.
DOI:10.1109/AIIoT58432.2024.10574669