DETEKSI ASPAL JALAN DAN KENDARAAN DISEKITAR MENGGUNAKAN SENSOR FUSION DENGAN HSV COLOR SPACE UNTUK ELECTRIC VEHICLE

GHAIDA, AULIA and Suprapto, Bhakti Yudho (2020) DETEKSI ASPAL JALAN DAN KENDARAAN DISEKITAR MENGGUNAKAN SENSOR FUSION DENGAN HSV COLOR SPACE UNTUK ELECTRIC VEHICLE. Undergraduate thesis, Sriwijaya University.

Preview	Text RAMA_20201_03041281520097_0011027503_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (1MB) | Preview
	Text RAMA_20201_03041281520097_0011027503_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (755kB) | Request a copy
	Text RAMA_20201_03041281520097_0011027503_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (412kB) | Request a copy
	Text RAMA_20201_03041281520097_0011027503_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (2MB) | Request a copy
	Text RAMA_20201_03041281520097_0011027503_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (186kB) | Request a copy
	Text RAMA_20201_03041281520097_0011027503_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (3MB) | Request a copy
	Text RAMA_20201_03041281520097_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (9MB) | Request a copy
	Text RAMA_20201_03041281520097.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (6MB) | Request a copy
	Text RAMA_20201_03041281520097_0011027503_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (504kB) | Request a copy

Abstract

The autonomous vehicle is a technology that is growing rapidly today. Autonomous vehicles require a movement system called the navigation system. Navigation systems on automatic vehicles usually use LIDAR, GPS, camera sensors, and also waypoints. Of the many navigation systems used, the use of camera sensors becomes the most dominating navigation system. Much research has been done in developing camera sensors to detect road and object detection. Generally, the color model used is RGB or grayscale image, but in this study, the use of HSV color space is used to recognize roads and objects. This study uses primary data of 7 videos in a predetermined area, namely the road around the Department of Architecture, Faculty of Engineering, Sriwijaya University. The data obtained is processed based on the stages that have been determined, for the front camera, right, and left using HSV color space, ROI (Region of Interest), Hough Transform, and Canny Edge Detection. To detect objects adding files in xml format is done to recognize objects with commands in the form of programs, while to determine the distance of the object used to input data in the form of an area of unreadable pixels obtained from the color of the vehicle. Research on asphalt road detection and distance of objects around using sensor fusion methods and HSV color space for autonomous vehicles was successfully conducted with an accuracy of 78,012 %% for asphalt road detection, 80% for detection of safe or unsafe areas, 80% for object detection, and 74, 76% for object distance detection. The results of this study are quite good but still have some deficiencies caused by lighting for asphalt detection of roads and lack of data on vehicle types in XML files for object detection.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Autonomous vehicle, road detection, HSV color space
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800-8360 Electronics
Divisions:	03-Faculty of Engineering > 20201-Electrical Engineering (S1)
Depositing User:	Users 5798 not found.
Date Deposited:	02 Jun 2020 02:38
Last Modified:	02 Jun 2020 02:38
URI:	http://repository.unsri.ac.id/id/eprint/29598

Actions (login required)

View Item