Print Email Facebook Twitter Deep End-to-end Network for 3D Object Detection in the Context of Autonomous Driving Title Deep End-to-end Network for 3D Object Detection in the Context of Autonomous Driving Author Jargot, Dominik (TU Delft Mechanical, Maritime and Materials Engineering; TU Delft Cognitive Robotics) Contributor Gavrila, Dariu (mentor) Roth, Markus (mentor) Kober, Jens (graduation committee) Kooij, Julian (graduation committee) Kok, Manon (graduation committee) Degree granting institution Delft University of Technology Programme Mechanical Engineering | Vehicle Engineering Date 2019-02-13 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. Subject 3D object detectionThesisIntelligent VehiclesDeep LearningMachine LearningCameraLidar To reference this document use: http://resolver.tudelft.nl/uuid:6389d77c-007d-455f-8e84-10a4f9b57a9d Part of collection Student theses Document type master thesis Rights © 2019 Dominik Jargot Files PDF MSc_Thesis_Report_Dominik ... Jargot.pdf 44.97 MB Close viewer /islandora/object/uuid:6389d77c-007d-455f-8e84-10a4f9b57a9d/datastream/OBJ/view