The Dataset I have used here is the German Traffic Sign Benchmark is a multi-class, single-image classification challenge held at the International Joint Conference on Neural Networks (IJCNN) 2011. Traffic sign detection is a high relevance computer vision problem and is the basis for a lot of applications in industry such as Automotive etc. Traffic signs can provide a wide range of variations between classes in terms of color, shape, and the presence of pictograms or text. In this project, I used deep neural networks and two classic convolutional neural network architectures (LeNet and AlexNet) to classify traffic signs. I will train and validate a model so it can classify traffic sign images using the German Traffic Sign Dataset. After the model is trained, I will then try out my model on images of German traffic signs that I find on the web.

The motivation for this project lays both personal interest in a better understanding for object detection and academic research. The goal is to develop a foundation for a roadsign-detection (RSD) with the option to add further objects or functions to it. The ultimate goal is to have useable object detection for the automotive sector.

The goals / steps of this project are the following:

1. Load and explore the data set.
2. Realize LeNet architecture and use ReLu, mini-batch gradient descent and dropout.
3. Realize AlexNet and make some modifications, use learning rate decay, Adam optimization and L2 regulization.
4. Analyze the softmax probabilities of the new images
5. Summarize the results