Jewellery AI
Automated jewellery Component Recognition
Intelligent Operations & AutomationAI Agents & Digital Workforce
Services
Computer Vision & Automation
Category
Jewellery Manufacturing
Client
Bernardo Manufacturing
Problem
Identifying jewellery components from images was a fully manual process, leading to delays and inefficiencies in quality control and inventory workflows.
Solution
Polaris developed a custom computer vision system trained to detect multiple jewellery components within a single image. The solution combined object detection, barcode scanning, OCR, and automated data extraction to replace manual inspection.
Outcome
Complete automation of component identification
Accurate detection in complex imagery
Successful barcode scanning and metadata extraction
Significant time savings across manufacturing operations
Technical Details
Computer Vision Engine: Deployed an ensemble of custom-trained YOLO (You Only Look Once) models, fine-tuned on proprietary datasets to detect intricate jewellery components and specific configurations within cluttered or complex images.
Multi-Modal Data Extraction: Integrated a hybrid analysis pipeline that combines object detection with Optical Character Recognition (OCR) and Barcode/QR Code scanning libraries. This allows the system to simultaneously identify physical items and extract metadata from attached labels.
Batch Processing Architecture: Engineered a high-throughput backend capable of ingesting bulk image uploads via a client web portal. The system queues and processes large volumes of images asynchronously to ensure stability and speed.
Automated Reporting: Implemented a notification service that aggregates detection results—including component counts, classification accuracy, and decoded metadata—into standardized reports delivered automatically via email for immediate operational use.
