Deep End-to-end Network for 3D Object Detection in the Context of Autonomous Driving

Master thesis (2019)

Authors

D. Jargot Mechanical Engineering

Contributors

D. Gavrila Intelligent Vehicles - Mechanical, Maritime and Materials Engineering (supervisor 1)
M. Roth Intelligent Vehicles - Mechanical, Maritime and Materials Engineering (supervisor 1)
J. Kober Learning & Autonomous Control - Mechanical, Maritime and Materials Engineering (supervisor 2)
J.F.P. Kooij Intelligent Vehicles - Mechanical, Maritime and Materials Engineering (supervisor 2)
M. Kok Team Jan-Willem van Wingerden - Mechanical, Maritime and Materials Engineering (supervisor 2)
Faculty
Mechanical Engineering
Copyright: © 2019 Dominik Jargot
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Published Date

13-02-2019

Language

English

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Faculty

Mechanical Engineering

Abstract

Nowadays, autonomous driving is a trending topic in the automotive field. One of the most crucial challenges of autonomous driving research is environment perception. Currently, many techniques achieve satisfactory performance in 2D object detection using camera images. Nevertheless, such 2D object detection might be not sufficient for autonomous driving applications as the vehicle is operating in a 3D world where all the dimensions have to be considered. In this thesis a new method for 3D object detection, using deep learning approach is presented. The proposed architecture is able to detect cars using data from images and point clouds. The proposed network does not use any hand-crafted features and is trained in an end-to-end manner. The network is trained and evaluated with the widely used KITTI dataset. The proposed method achieves an average precision of 81.38%, 67.02%, and 65.30% on the easy, moderate, and hard subsets of the KITTI validation dataset, respectively. The average inference time per scene is 0.2 seconds.