DESAIN APLIKASI DETEKSI DAN ESTIMASI DIMENSI LUBANG BERBASIS FLUTTER DAN DEEP LEARNING

WIBOWO, ADITYA ERLANGGA and Dwijayanti, Suci (2024) DESAIN APLIKASI DETEKSI DAN ESTIMASI DIMENSI LUBANG BERBASIS FLUTTER DAN DEEP LEARNING. Undergraduate thesis, Sriwijaya University.

	Text RAMA_20201_03041282025055.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (5MB) | Request a copy
	Text RAMA_20201_03041282025055_TURNITIN.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (11MB) | Request a copy
	Text RAMA_20201_03041282025055_0030078404_01_front_ref.pdf - Accepted Version Available under License Creative Commons Public Domain Dedication. Download (1MB)
	Text RAMA_20201_03041282025055_0030078404_02.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (575kB) | Request a copy
	Text RAMA_20201_03041282025055_0030078404_03.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (765kB) | Request a copy
	Text RAMA_20201_03041282025055_0030078404_04.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (1MB) | Request a copy
	Text RAMA_20201_03041282025055_0030078404_05.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (204kB) | Request a copy
	Text RAMA_20201_03041282025055_0030078404_06_ref.pdf - Bibliography Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (205kB) | Request a copy
	Text RAMA_20201_03041282025055_0030078404_07_lamp.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Public Domain Dedication. Download (2MB) | Request a copy

Abstract

Road damage, particularly potholes, can cause discomfort and increase the risk of accidents. Rapid and accurate identification of road conditions is essential for immediate repairs. However, pothole identification is still performed manually, necessitating the automation of pothole detection using digital image processing and deep learning to detect and estimate pothole dimension. This research aims to design a pothole detection and dimension estimation application based on Flutter and deep learning, namely POTION AI. The application uses YOLOv8 and Mask R-CNN algorithms to detect potholes and measure their dimensions. Training data for the model were collected using a GoPro Hero 8 camera in Sumatra Selatan, including Jl. Ariodillah, Jl. Kaca Piring, Jl. Swakarya I, Jl. Swakarya II, Jl. Dwikora II, and Jl. Kampung Bali. The training process was conducted on Google Colaboratory using YOLOv8x-seg, the largest model of YOLOv8, with 71 million parameters. Research results show that the YOLOv8 and Mask R-CNN models can detect potholes with high accuracy, achieving a confidence score above 92.22%, and performing consistently well on both local systems and mobile applications. The application testing was carried out by integrating both models, YOLOv8 and Mask R-CNN, into a Flutter-based application to detect and estimate pothole dimensions. The application also uses Leaflet JS to display an interactive map showing the detected pothole locations. Testing results indicate that the POTION AI application functions well on various devices and provides accurate information about road conditions, achieving a final usability score of 4.8875. This application is expected to help expedite road repairs and reduce accidents caused by potholes.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Pothole Detection Application, Pothole Dimension Estimation, Segmentation, YOLO, Mask R-CNN
Subjects:	T Technology > TE Highway engineering. Roads and pavements > TE1-450 Highway engineering. Roads and pavements T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK1-9971 Electrical engineering. Electronics. Nuclear engineering > TK1 Electrical engineering--Periodicals. Automatic control--Periodicals. Computer science--Periodicals. Information technology--Periodicals. Automatic control. Computer science. Electrical engineering. Information technology. T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television > TK5105.5.S72 Computer networks T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7885-7895 Computer engineering. Computer hardware T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK9001-9401 Nuclear engineering. Atomic power T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK9900-9971 Electricity for amateurs. Amateur constructors' manuals
Divisions:	03-Faculty of Engineering > 20201-Electrical Engineering (S1)
Depositing User:	Aditya Erlangga Wibowo
Date Deposited:	18 Jul 2024 02:37
Last Modified:	18 Jul 2024 02:37
URI:	http://repository.unsri.ac.id/id/eprint/151491

Actions (login required)

View Item