A drum labeling system and rotary drum labeling machine based on AI vision
The AI vision-based barrel labeling system utilizes a single top-mounted vision acquisition unit in conjunction with a ring-shaped reflector to achieve precise identification and positioning of the circumferential seams of the barrel. This solves the problems of high cost, low accuracy, and poor stability caused by multi-camera layouts, improves labeling accuracy and equipment stability, and adapts to the automated production needs of barrels of different specifications.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ZHUHAI LONGTEC CORP LTD
- Filing Date
- 2026-05-07
- Publication Date
- 2026-06-02
AI Technical Summary
In existing industrial drum labeling technologies, multi-camera distributed recognition solutions suffer from problems such as high hardware costs, low accuracy, cumbersome debugging, poor stability, and bloated structure, failing to meet the demands of high-precision, high-efficiency, and low-cost automated production.
The system employs an AI vision-based barrel labeling system, which includes a central control module, a barrel conveying module, a circumferential barrel rotating module, an AI optical reflection vision module, and a labeling execution module. Through a single top vision acquisition unit working in conjunction with a ring reflector, it achieves panoramic image acquisition and precise positioning of the barrel's circumferential seams. Combined with parameter adaptation and anomaly warning modules, it ensures system stability and accuracy.
It significantly reduces hardware and maintenance costs, improves positioning and labeling accuracy, has high system integration, optimizes space utilization, and is compatible with different sizes of barrels. It solves the problems of high cost, cumbersome debugging, and large errors in traditional multi-camera layouts, and improves labeling pass rate and equipment stability.
Smart Images

Figure CN122126538A_ABST
Abstract
Citation Information
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