SignalGrab: A Machine Learning Approach to Helping Color Blind Drivers

Overview

There are nearly fourteen million individuals with color vision deficiency (color blindness) in the United States. Many individuals with color blindness struggle with differentiating between red and green traffic signals, particularly at night when other v isual cues are unavailable. This can lead to traffic hazards imposing risk for both them and those around them. SignalGrab is a novel approach to solving this problem through a mobile Android app that drivers can easily use to help with recognizing traffic signals. SignalGrab uses a machine learning image recognition model built using TensorFlow and trained on a custom dataset of traffic signal images. The model is embedded in an Android app built using Android Studio. The app has a minimalistic design and provides audible information about the type of approaching traffic lights to avoid driver distractions. SignalGrab reliably solves the issue of traffic light recognition for color-blind drivers, and in real-life testing, it correctly recognizes traffic signals with an accuracy of 97.1 percent.

Competition history

• JSHS 2024 Category not listed

Resources

• National JSHS abstract book ↗

Related projects

Closest projects by meaning, across every fair and year in the corpus.