Automation of Road Sign Labeling Using Transfer Learning and Computer Vision for Autonomous Driving Analysis

The client is currently engaged in analyzing road recordings to identify and annotate various objects such as traffic signs. Manual labeling is labor-intensive, time-consuming, and limits the volume of data processed, hindering scalability and timely deployment of autonomous driving systems.

A mid to large-sized automotive technology firm developing autonomous vehicle systems requiring large-scale road environment analysis.

• Develop and deploy machine learning models capable of detecting and classifying road signs in video recordings.
• Reduce manual labeling time from multiple days to minutes per recording.
• Minimize human verification effort, requiring only a single reviewer to validate model predictions.
• Enable processing of a higher volume of road recordings simultaneously, accelerating data collection and model retraining cycles.
• Leverage transfer learning to minimize data requirements and optimize training efficiency.

• Detection Module: Use pre-trained neural networks with transfer learning to identify all visible road signs in video frames.
• Recognition Module: Export detected signs as images for further classification.
• Classification Module: Employ a second model trained to accurately assign labels and meanings to detected road signs.
• Integrated Pipeline: Seamless processing of entire video recordings with results ready for review within seconds.
• Annotation Export: Export detection and classification results for human review and validation.
• Model Retraining Support: Incorporate new labeled data to continuously improve model accuracy.

• Video data ingestion from storage or live feeds
• Exporting detection and classification results to internal data management systems
• Optional integration with human review platforms for verification

• Processing speed: Complete analysis of recordings within seconds to minutes
• Scalability: Support for processing large volumes of video data concurrently
• Accuracy: High detection and classification precision with minimal false positives/negatives
• Security: Data protection for sensitive video and image data
• Maintainability: Modular design for easy updates and retraining

The implementation of automated detection and classification models is expected to significantly reduce labeling time from several days to under an hour per recording. This will enable processing larger datasets, accelerate model development cycles, and reduce labor costs—potentially increasing throughput by over 400%, while maintaining high accuracy levels. The streamlined workflow will also facilitate faster deployment of autonomous driving models and improve overall data quality and consistency.