GreenEye: Development of Real-Time Traffic Signal Recognition System for Visual Impairments
This addresses a critical safety challenge for visually impaired pedestrians by enabling real-time recognition of traffic signals and crossing times, though it is incremental as it builds on prior work focused only on color recognition.
The researchers tackled the problem of real-time traffic signal recognition for visually impaired people by developing the GreenEye system, which achieved 99.5% precision in recognizing 14 classes including signal color and time left to cross after addressing data imbalance.
Recognizing a traffic signal, determining if the signal is green or red, and figuring out the time left to cross the crosswalk are significant challenges to visually impaired people. Previous research has focused on recognizing only two traffic signals, green and red lights, using machine learning techniques. The proposed method developed a GreenEye system that recognizes the traffic signals' color and tells the time left for pedestrians to cross the crosswalk in real-time. GreenEye's first training showed the highest precision of 74.6%; four classes reported 40% or lower recognition precision in this training session. The data imbalance caused low precision; thus, extra labeling and database formation were performed to stabilize the number of images between different classes. After the stabilization, all 14 classes showed excelling precision rate of 99.5%.