用视觉识别自动检测金枪鱼罐头缺陷，提升质检效率
Automated Quality Control System for Canned Tuna Production using Artificial Vision
- 通过相机+传感器+机械臂实现罐头缺陷实时识别与分类
- YOLOv5模型准确检测罐盖瑕疵和易开环位置，识别率达98.6%
- 系统支持工业4.0集成，适合食品制造企业自动化升级
本文提出一种基于人工智能视觉的自动化质量控制系统，用于检测金枪鱼金属罐头的缺陷。系统通过传送带与光电传感器触发摄像头进行视觉采集，利用机械臂根据罐体状态分类。通过物联网架构（Mosquitto、Node-RED、InfluxDB、Grafana）实现工业4.0集成。采用YOLOv5模型检测罐盖缺陷及易开环位置，结合谷歌Colab GPU训练实现标签文字识别（OCR）。实验表明，该系统可实现实时问题识别，优化资源使用，保障产品质量，同时提升质检环节自动化水平，使操作员得以从事其他任务。
原文摘要 · Abstract (English)
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.
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