The client is a leading Tier-1 provider of automotive software engineering services, specializing in autonomous driving and advanced driver-assistance systems (ADAS). Known for its technical depth in computer vision, sensor fusion, and perception engineering, the client supports global OEMs and mobility technology providers in building robust, production-grade autonomous systems. With a large volume of multi-sensor data flowing from test vehicles, the client was focused on improving its data pipeline throughput while ensuring labeling precision.

Challenge: Training high-performing ADAS perception models requires vast quantities of annotated image and video data. Manual labeling is labor-intensive, error-prone, and difficult to scale – especially when annotation requirements span diverse driving scenarios, lighting conditions, and object classes. The client needed an intelligent, automated annotation solution that could:

• Drastically reduce manual labeling time and effort
• Handle varying visual conditions such as low light or motion blur
• Deliver accurate annotations for key ADAS-relevant objects
• Ensure GDPR-compliant anonymization of sensitive data
• Seamlessly integrate with existing annotation tools and data pipelines

Solutions: ALTEN India designed and developed a powerful AI/ML-driven annotation engine, embedded into the client’s existing data processing workflow. The solution involved several stages of intelligent preprocessing and deep learning-powered object detection:

1. Image Preprocessing for Quality Normalization
   • Gamma Correction: Adjusted brightness across image frames to normalize lighting and improve model consistency
   • Image Smoothing: Applied localized averaging to remove image noise, enhancing object detection accuracy
2. Smart ROI and Feature Isolation
   • Region of Interest (ROI) Detection: Leveraged ML to identify high-probability zones containing relevant objects (e.g., vehicles, lane markings)
   • Color Filtering: Enabled segmentation of traffic signs, construction cones, and road features based on predefined color signatures
   • Edge Detection: Used for boundary delineation of lane lines, road signs, and vehicles, improving spatial annotation accuracy
3. Deep Learning-Based Object Detection and Labeling
   • Advanced Object Recognition: Deployed TensorFlow-based Faster R-CNN models to accurately detect and label:
     • Traffic signs and road infrastructure
     • Vehicles and license plates
     • Lane boundaries and lane width measurements
     • Pedestrians and cyclists
4. Anonymization for Regulatory Compliance
   • Implemented real-time anonymization of human figures and vehicle license plates using computer vision filters and overlays, ensuring GDPR compliance across European data collection zones

Benefits:

• Reduced Annotation Time: Substantially minimized manual workload through automated detection, improving throughput and time-to-model
• Higher Ground Truth Accuracy: Improved consistency and accuracy of annotations reduced downstream training errors and false positives
• Scalable Architecture: The modular engine was compatible with multiple annotation tools, enabling deployment across data lakes and large-scale projects
• Privacy-Compliant Data Handling: Built-in anonymization features ensured global compliance and safeguarded personal data integrity