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02349nam a22002417a 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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FT8776 |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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20251111132419.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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251111b ||||| |||| 00| 0 eng d |
| 041 ## - LANGUAGE CODE |
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| Language code of text/sound track or separate title |
engtag |
| 050 ## - LIBRARY OF CONGRESS CALL NUMBER |
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| Classification number |
T58 B35 2025 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
. |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Baluyot, Duraemond O.; Cunanan, Juan Ryan Gabriel M.; Gatdula, Matthew Cypres Y. |
| 245 ## - TITLE STATEMENT |
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| Title |
Vguard: Development of mobile real-time vehicle damage detection application with image recognition using YOLOV8 |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE |
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| Place of production, publication, distribution, manufacture |
. |
| Name of producer, publisher, distributor, manufacturer |
. |
| Date of production, publication, distribution, manufacture, or copyright notice |
c2025 |
| 300 ## - PHYSICAL DESCRIPTION |
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Capstone Project: (Bachelor of Science in Information Technology) - Pamantasan ng Lungsod ng Maynila, 2025 |
| 336 ## - CONTENT TYPE |
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text |
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text |
| Content type code |
text |
| 337 ## - MEDIA TYPE |
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unmediated |
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unmediated |
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unmediated |
| 338 ## - CARRIER TYPE |
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volume |
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volume |
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volume |
| 505 ## - FORMATTED CONTENTS NOTE |
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| Formatted contents note |
ABSTRACT: Traditionally, vehicle inspections rely on manual procedures that often lack precision, efficiency, and visual feedback. To address these limitations, this study developed VGuard, a mobile application that enhances vehicle diagnostics by integrating automated engine data retrieval, real-time image recognition, and visual representation of system status and faults. VGuard highlights three core features: (1) real-time engine data retrieval using an ELM327 OBD-II scanner diagnostic trouble codes and sensor data from the Engine Control Unit (ECU); (2) exterior damage detection using YOLOv8, trained on a dataset of 4,609 labeled images of dents, scratches, cracks, and rust; and (3) dual visualization, which includes a 2D fault display and a 3D car model for better interpretation of both internal issues. The trained YOLOv8 model achieved a mean Average Precision (mAP) of 72.1%, precision of 82.7%, and recall of 63.7%, allowing the system to recognize vehicle surface damages accurately. However, the model showed a higher rate of false positives for scratches and background elements, which may be attributed to poor lighting, image noise, and limited training diversity. The integration of OBD-II diagnosis, intelligent image recognition, and informative visual displays improved the usability of the system and made vehicle condition assessment more accessible. Overall, VGuard successfully met its development goals and presents a promising mobile solution for real-time vehicle damage detection and diagnosis. |
| 526 ## - STUDY PROGRAM INFORMATION NOTE |
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| Classification |
Filipiniana |
| 655 ## - INDEX TERM--GENRE/FORM |
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| Genre/form data or focus term |
academic writing |
| 942 ## - ADDED ENTRY ELEMENTS |
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| Item type |
Thesis/Dissertation |