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Traffic-Sign-Detection is a machine learning project that focuses on detecting and classifying traffic signs in images. Using computer vision and deep learning techniques, this project can identify various types of traffic signs, making it useful for applications like autonomous driving systems and traffic monitoring.

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Traffic - CS50 AI With Python

By Roi Solomon

I used the The German Traffic Sign Recognition Benchmark (GTSRB) database.

What I Found?

Tested the effects of hidden layers on the accuracy of the model.

Hidden Layers:

        # Another hidden layer
        tf.keras.layers.Dense(NUM_CATEGORIES * 16, activation="relu"),

        # Another hidden layer
        tf.keras.layers.Dense(NUM_CATEGORIES * 8, activation="relu"),

        # Another hidden layer
        tf.keras.layers.Dense(NUM_CATEGORIES * 4, activation="relu"),

Result - 97% Acccuracy:

GTSRB Database:

The database contains 43 subdirectories in this dataset, numbered 0 through 42. Each numbered subdirectory represents a different category (a different type of road sign). Within each traffic sign’s directory is a collection of images of that type of traffic sign.

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Sources

  • TensorFlow The Sequential model - Link
  • Loading Custom Image Dataset for Deep Learning Models: Part 1 - Link
  • Understand the Softmax Function in Minutes - Link
  • CS50 Lecture 5 Notes - Link

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MIT

About

Traffic-Sign-Detection is a machine learning project that focuses on detecting and classifying traffic signs in images. Using computer vision and deep learning techniques, this project can identify various types of traffic signs, making it useful for applications like autonomous driving systems and traffic monitoring.

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