3D StixelNet Deep Neural Network for 3D object detection stixel-based

Capuzzo, Davide (2020) 3D StixelNet Deep Neural Network for 3D object detection stixel-based. [Laurea magistrale], Università di Bologna, Corso di Studio in Advanced automotive electronic engineering [LM-DM270]
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In this thesis it has been presented an algorithm of deep learning for 3D object detection from the point cloud in an outdoor environment. This algorithm is feed with stixel, a medium-type data generates starting from a point cloud or depth map. A stixel can be think as a small rectangle that start form the base of the road and then rises until the top of the obstacle summarizing the vertical surface of an object. The goal of stixel is to compress the data coming from sensors in order to have a fast transmission without losing information. The algorithm to generate stixel is a novel algorithm developed by myself that is able to be applied both from point cloud generated by lidar and also from depth map generated by stereo and mono camera. The main passage to create this type of data are: the elimination of points that lied on ground plane; the creation of an average matrix that summarizes the depth of group of stixel; the creation of stixel merging all the cells that are of the same object. The stixel generates reduce the points from 40 000 to 1200 for LIDAR point cloud and to 480 000 to 1200 for depth map. In order to extract 3D information from stixel this data has been feed into a deep learning algorithm adapted to receive as input this type of data. The adaptation has been made starting from an existing neural network use for 3D object detection in an indoor environment. This network has been adapted in order to overcome the sparsity of data and to the big size of the scene. Despite the reduction of the number of data, thanks to the right tuning the network created in this thesis have been able to achieve the state of the art for 3D object detection. This is a relevant result because it opens the way to the use of medium-type data and underlines that the reduction of points does not mean a reduction of information if the data are compressed in a smart way. oints not means a reduction of information if the data are compressed in a smart way.

Tipologia del documento
Tesi di laurea (Laurea magistrale)
Autore della tesi
Capuzzo, Davide
Relatore della tesi
Correlatore della tesi
Corso di studio
Ordinamento Cds
Parole chiave
3D object detection,autonomous driving,self-driving car,computer vision,neural network,deep learning,stixel,3DstixelNet,Kitti Dataset,pose estimation,medium type data
Data di discussione della Tesi
3 Dicembre 2020

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