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Automated Road Sign Detection and Classification System for Autonomous Driving Analysis

spyro-soft.com

Information technology

Automotive

Transportation

Manual Labeling Bottleneck

The client faced excessive manual labor requirements for labeling road signs in autonomous driving video recordings, requiring 4 employees working for days to complete analysis tasks with subsequent verification delays

About the Client

A technology company developing AI-powered analysis tools for autonomous vehicle systems

Automation Goals

Develop machine learning models for automated road sign detection
Implement transfer learning to reduce training data requirements
Create a processing pipeline for end-to-end sign detection and classification
Reduce labeling time from days to minutes

Core System Capabilities

Object detection for identifying road signs in video frames
Image classification for sign meaning recognition
Transfer learning implementation with pre-trained neural networks
Automated result verification interface
Batch processing pipeline for multiple video recordings

Technology Stack

Convolutional Neural Networks (CNN)

Transfer learning frameworks

Cloud-based processing infrastructure

Computer vision libraries (OpenCV, TensorFlow)

System Integrations

Existing autonomous driving data storage systems
Cloud-based model training platforms
Human verification interface for label validation

Operational Requirements

High-throughput video processing capability
Model accuracy above 95% for sign classification
Scalable cloud architecture for parallel processing
Low-latency inference for real-time applications

Operational Efficiency Improvement

Automated system reduces 4-person days of manual work to minutes with single-person verification, enabling 10x faster data processing and accelerating autonomous driving system development cycles