Summary of Automated Quality Control System For Canned Tuna Production Using Artificial Vision, by Sendey Vera et al.
Automated Quality Control System for Canned Tuna Production using Artificial Vision
by Sendey Vera, Luis Chuquimarca, Wilson Galdea, Bremnen Véliz, Carlos Saldaña
First submitted to arxiv on: 8 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Robotics (cs.RO); Image and Video Processing (eess.IV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper presents a novel automated control system for detecting and classifying faults in tuna metal cans using artificial vision. The system consists of a conveyor belt, camera, photoelectric sensor, and robotic arm, which work together to classify metal cans based on their condition. The authors leverage Industry 4.0 integration through IoT technologies such as Mosquitto, Node-RED, InfluxDB, and Grafana. Specifically, they employ the YOLOv5 model for detecting faults in metal can lids and easy-open rings. Training the model with GPU on Google Colab enables OCR text detection on labels. The results show efficient real-time problem identification, optimized resource utilization, and high-quality product delivery. Additionally, the vision system contributes to autonomy in quality control tasks, freeing operators to perform other functions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a smart system that uses AI to inspect tuna cans for defects and sorts them into different categories. This helps factories make better products faster and with less waste. The system has three main parts: a belt that moves the cans along, a camera that takes pictures of each can, and an arm that picks up the cans based on their condition. To make this work, the researchers use special tools like YOLOv5 and Mosquitto to detect problems in the cans and sort them quickly. This makes it easier for workers to focus on other tasks. |
