Geopavenet: Automated road pavement damage classifaction and severity identification with geolocation using jetson nao and yolov8/ Julius Nikolai D. Bernado, Victor Sebastian D. Bondoc, Renato Jr. T. Panis, Efren Jr. D. Pastores, Gary Clyde T. Rabe, and Jevon A. Silvano.--

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Bernado, Julius Nikolai D [author]

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Material type: Text

TextPublication details: Manila: Technological University of the Philippines, 2025.Description: ix, 138pages: 29cmContent type:

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BTH TK 870 B47 2025

Dissertation note: College of Engineering.-- Bachelor of science in electronics engineering: Technological University of the Philippines, 2025. Summary: In the Philippines, road pavement damage poses significant threats to road safety, often leading to accidents, vehicular damage, and increased maintenance costs. Existing road survey methods that check pavement damage can take time, require significant labor, can be expensive, and make subjective evaluations, hurting accuracy and efficiency through delay in maintenance and repair on the damage that is occurring. GeoPaveNet is designed to innovate the traditional method of inspecting road pavement damages by using deep learning models, and geolocation; creating an automated, real-time system shifting traditional road pavement assessments using artificial intelligence (AI). Intended to be a potential adaptation by the Department of Public Works and Highways (DPWH), offering an innovative approach in improving road condition assessments and streamline maintenance planning processes. Using a moving vehicle, a high resolution camera is mounted capturing images of road pavement damages in real-time, processed by the NVIDIA Jetson Nano, YOLOv8 as the deep-learning model classifying road pavement damage classes: potholes, cracks, alligator cracks, pumping and depression, with severity levels determined based on the criteria provided in the DPWH Guides and Standards for Labelling Road Damage, which include factors such as crack width, depth, deformation, and damage pattern. A VK-162 GPS module was included to geotag each detected road damage by taking longitudinal and latitude location coordinates in real time for accurate location mapping and comprehensive reporting of the conditions of the roads. The trained object detection model had a mean Average Precision (mAP) of 0.423 after 100 epochs of training, which reflects the model's ability to detect and classify multiple types of pavement defects for practical, real-world deployment.

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Item type	Current library	Shelving location	Call number	Copy number	Status	Date due	Barcode
Bachelor's Thesis COE	TUP Manila Library	Thesis Section-2nd floor	BTH TK 870 B47 2025 (Browse shelf(Opens below))	c.1.	Not for loan		BTH0006456

Bachelor's thesis

College of Engineering.-- Bachelor of science in electronics engineering: Technological University of the Philippines, 2025.

Includes bibliographic references and index.

In the Philippines, road pavement damage poses significant threats to road safety,
often leading to accidents, vehicular damage, and increased maintenance costs. Existing
road survey methods that check pavement damage can take time, require significant
labor, can be expensive, and make subjective evaluations, hurting accuracy and efficiency
through delay in maintenance and repair on the damage that is occurring. GeoPaveNet is
designed to innovate the traditional method of inspecting road pavement damages by
using deep learning models, and geolocation; creating an automated, real-time system
shifting traditional road pavement assessments using artificial intelligence (AI). Intended
to be a potential adaptation by the Department of Public Works and Highways (DPWH),
offering an innovative approach in improving road condition assessments and streamline
maintenance planning processes. Using a moving vehicle, a high resolution camera is
mounted capturing images of road pavement damages in real-time, processed by the
NVIDIA Jetson Nano, YOLOv8 as the deep-learning model classifying road pavement
damage classes: potholes, cracks, alligator cracks, pumping and depression, with severity
levels determined based on the criteria provided in the DPWH Guides and Standards for
Labelling Road Damage, which include factors such as crack width, depth, deformation,
and damage pattern. A VK-162 GPS module was included to geotag each detected road
damage by taking longitudinal and latitude location coordinates in real time for accurate
location mapping and comprehensive reporting of the conditions of the roads. The trained
object detection model had a mean Average Precision (mAP) of 0.423 after 100 epochs of
training, which reflects the model's ability to detect and classify multiple types of
pavement defects for practical, real-world deployment.

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