Drones can now detect tags at greater distance due to custom dataset preparation

We delivered a comprehensive data pipeline and detection system for visual marker (Tag) identification used across robotics, drones, and logistics industries. Our project addressed the challenge of unreliable tag detection in real-world conditions through strategic data generation, quality enhancement, and custom detection algorithms.
System Analysis & Data Assessment: Our team conducted detailed analysis of existing tag detection systems, evaluating their performance limitations and data dependencies. We identified specific data gaps and quality issues that were limiting detection reliability in industrial environments.
Synthetic Data Generation Pipeline: We developed a custom synthetic data generator capable of producing realistic, varied training samples that addressed the data scarcity challenges common in industrial marker detection. This data generation system created diverse training scenarios that improved model robustness across different lighting conditions, angles, and environmental factors.
Custom Detection Data Architecture: Our team built a tailored object detection system with specialized regression components optimized for precise marker localization. We designed the data processing pipeline to handle the unique characteristics of industrial visual markers while maintaining high accuracy standards.
End-to-End Data Integration: We integrated the complete data pipeline—from synthetic data generation through detection and classification—into a production-ready system. Our approach ensured seamless data flow and consistent quality throughout the entire detection process.
Performance Validation: The client confirmed that our solution outperformed all previous implementations, delivering measurably higher accuracy and reliability in real-world deployment scenarios.