Color Segmentation on FPGA Using Minimum Distance Classifier for Automatic Road Sign Detection

TL;DR

This paper presents an FPGA-based minimum distance classifier optimized with pipelining for fast, energy-efficient color segmentation and road sign detection, adaptable to various feature space dimensions.

Contribution

It introduces a reconfigurable FPGA implementation of a minimum distance classifier with a multi-class labeling module for real-time road sign detection.

Findings

Efficient FPGA implementation of color segmentation classifier.

Successful detection of road signs using combined classification and labeling.

Flexible design adaptable to different feature space dimensions.

Abstract

Classification is an important step in machine vision systems; it reveals the true identity of an object using features extracted in pre-processing steps. Practical usage requires the operation to be fast, energy efficient and easy to implement. In this paper, we present a design of the Minimum Distance Classifier based on an FPGA platform. It is optimized by the pipelined structure to strike a balance between device utilization and computational speed. In addition, the dimension of the feature space is modeled as a generic parameter, making it possible for the design to re-generate hardware to cope with feature space with arbitrary dimensions. Its primary application is demonstrated in color segmentation on FPGA in the form of efficient classification using color as a feature. This result is further extended by introducing a multi-class component labeling module to label the segmented color components and measure their geometric properties. The combination of these two modules can effectively detect road signs as the region of interests.