A 92mW real-time traffic sign recognition system with robust light and dark adaptation

A traffic sign recognition system that is robust under various lighting condition is proposed with an image enhancement preprocessor and a recognition processor. The image enhancement preprocessor performs the Multi-scale Retinex (MSR) algorithm for robust light and dark adaptation. It includes a mixed-mode Adaptive Neuro-Fuzzy Inference System (ANFIS) engine that performs online optimizations for various scenes. The recognition processor performs the Support Vector Machine (SVM) algorithm for robust sign recognition. Its proposed algorithm-optimized kernel cache and memory architecture reduces the power consumption and memory redundancy by 78% and 35%, respectively. The proposed system is implemented in a 0.13μm CMOS process and is connected using Network-on-Chip (NoC) communication. As a result, the system achieves robust sign recognition under various lighting conditions while consuming just 92mW at 1.2V.