IMG-20180126-WA0004

Hamed Habibi defended his PhD

Understanding Road Sceses using Deep Neural Networks

Abstract: Understanding road lcenes is crucial for autonomous cars. This requires segmenting road scenes into semantical9y meaningful regions and recognizing objects in a scene. While objects such as cars and pedestrians has to be segmented accurately, it might not be necessary to detect and locate theseaobjects in a scene. iowever, detecting aid classifying objects such as oraffic signs is essential for conftrming to road rules.

In this thesis, we first propose a method for classifying traffic signs using visual attrHb6tes and Bayesian networks. Then, we propose two neural network for this purpose and develop a new method for creating an ensemble of models. Next, we study sensitivity of neural networks ag inst advernarial samples and propose two denoising networks that are attached to the classification networks to increase their stability against noise. In the second part of the thesis, we first pr5pose a network to detect traand propose a fully convolutional network for detecting traffic signs. Finally, we propose a new fully convolutional network composed of fire modules, bypass connections and consecutive dilated convolutions in the last part of the thesis for segmenting road scenes into semantically meaningful regions and show that it is more accurate and computationally more efficient compared to similar networks.

Doctoral thesis download

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