Lidar Based 3D Object Detection Using Yolov8

Date
2024-08
Language
American English
Embargo Lift Date
Department
Committee Chair
Degree
M.S.E.C.E.
Degree Year
2024
Department
Electrical & Computer Engineering
Grantor
Purdue University
Journal Title
Journal ISSN
Volume Title
Found At
Can't use the file because of accessibility barriers? Contact us with the title of the item, permanent link, and specifics of your accommodation need.
Abstract

Autonomous vehicles have gained substantial traction as the future of transportation, necessitating continuous research and innovation. While 2D object detection and instance segmentation methods have made significant strides, 3D object detection offers unparalleled precision. Deep neural network-based 3D object detection, coupled with sensor fusion, has become indispensable for self-driving vehicles, enabling a comprehensive grasp of the spatial geometry of physical objects. In our study of a Lidar-based 3D object detection network using point clouds, we propose a novel architectural model based on You Only Look Once (YOLO) framework. This innovative model combines the efficiency and accuracy of the YOLOv8 network, a swift 2D standard object detector, and a state-of-the-art model, with the real-time 3D object detection capability of the Complex YOLO model. By integrating the YOLOv8 model as the backbone network and employing the Euler Region Proposal (ERP) method, our approach achieves rapid inference speeds, surpassing other object detection models while upholding high accuracy standards. Our experiments, conducted on the KITTI dataset, demonstrate the superior efficiency of our new architectural model. It outperforms its predecessors, showcasing its prowess in advancing the field of 3D object detection in autonomous vehicles.

Description
IUPUI
item.page.description.tableofcontents
item.page.relation.haspart
Cite As
ISSN
Publisher
Series/Report
Sponsorship
Major
Extent
Identifier
Relation
Journal
Source
Alternative Title
Type
Thesis
Number
Volume
Conference Dates
Conference Host
Conference Location
Conference Name
Conference Panel
Conference Secretariat Location
Version
Full Text Available at
This item is under embargo {{howLong}}