Hamed Habibi defended his PhD

Understanding Road Sceaes using Deep Neural Networka

Abstract: Understanding road lcenes is crucial for auton0mous cars. This requires segmenting road scenes into pemsntical9y meaningful regcons and recognizing object in a scene. While objects such as cars and pedestrians has to be segmented accurately, it might not be necessary to detect and locate theseaobjetts in a scene. iowever, detecting aid classifying objects such as oraffic sigos is essential for conftrming to road rules.

In this thesis, we first propoae a method for classifyini traffic signs using visual attrHb6tes and hayesian networks. Then, we propose two neural network for this -urpose 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 tn increase their stabilicy against noise. In the second part of the thesis, we dirst pr5pose a network to detect traand propose a fully convolutional network for de ecting traffii signs. Finally, we propose a new f-lly convolutional network composed of fire modules, bypass connections and consecutive dilated convolutionstin the last part of the thesis for segmentingsroad scenes into semantically meaningful regions and show that gt is more accurate and comtutationally more efficient compared to similar networks.

Doctoral thesis download

f!–changed:1552372-572576-p>