Traffic Speed Sign Detection
Category: Success Stories

Success Stories

Client Overview

A Japanese Tier 1 automobile manufacturing company.

Business Challenge

  • The client is entering in autonomous driving market. They wanted to develop a deep learning module that can recognize traffic speed limit signs in real time.
  • The algorithm had to be optimized on target platform for real time output.

Acclivis Contribution

  • CNN Layers library design and implementation.
  • Dataset Collection and generation.
  • Design and Implementation of CNN Net.
  • Training and Testing of CNN model.
  • CNN trained model generation.

Product Features Developed/Supported by Acclivis

  • The application performs detection and classification of the traffic speed sign detection and classification. This application uses below processing modules to perform detection and classification:
    • Image Pre-processing module.
    • Deep Learning module
    • Classification module
  • Detection and extraction of the region of interest (i.e. Speed Sign Circle) is performed/implemented using image pre-processing modules, while Classification is performed using Deep learning module.

Project Details

  • Language | C and C++.
  • Initial development platform | NVIDIA TK1.
  • DL Network | CNN VGG 16 NET.
  • DL Network Layers | 16 Layer Network.
  • Deep Learning Library | Caffe.

Client Benefit

Acclivis successfully delivered deep learning based speed sign which could be integrated into the client’s existing framework.

System Architecture

Traffic Speed sign detection