A 92-mW Real-Time Traffic Sign Recognition System With Robust Illumination Adaptation and Support Vector Machine

Abstract

A low-power real-time traffic sign recognition system that is robust under various illumination conditions is proposed. It is composed of a Retinex preprocessor and an SVM processor. The Retinex preprocessor performs the Multi-Scale Retinex (MSR) algorithm for robust light and dark adaptation under harsh illumination environments. In the Retinex preprocessor, the recursive Gaussian engine (RGE) and reflectance engine (RE) exploit parallelism of the MSR tasks with a two-stage pipeline, and a mixed-mode scale generator (SG) with adaptive neuro-fuzzy inference system (ANFIS) performs parameter optimizations for various scene conditions. The SVM processor performs the SVM algorithm for robust traffic sign classification. The proposed algorithm-optimized small-sized kernel cache and memory controller reduce power consumption and memory redundancy by 78% and 35%, respectively. The proposed system is implemented as two separated ICs in a 0.13-mu m CMOS process, and the two chips are connected using network-on-chip off-chip gateway. The system achieves robust sign recognition operation with 90% sign recognition accuracy under harsh illumination conditions while consuming just 92 mW at 1.2 V.