000			03279nam a22003377a 4500
003			OSt
005			20250714183118.0
008			250714b |||||||| |||| 00| 0 eng d
040			_aTUPM _bEnglish _cTUPM _dTUPM _erda
050			_aBTH TK 870 _bB47 2025
100			_aBernado, Julius Nikolai D. _eauthor
245			_aGeopavenet: _bAutomated road pavement damage classifaction and severity identification with geolocation using jetson nao and yolov8/ _cJulius Nikolai D. Bernado, Victor Sebastian D. Bondoc, Renato Jr. T. Panis, Efren Jr. D. Pastores, Gary Clyde T. Rabe, and Jevon A. Silvano.--
260			_aManila: _bTechnological University of the Philippines, _c2025.
300			_aix, 138pages: _c29cm.
336			_2rdacontent
337			_2rdamedia
338			_2rdacarrier
500			_aBachelor's thesis
502			_aCollege of Engineering.-- _bBachelor of science in electronics engineering: _cTechnological University of the Philippines, _d2025.
504			_aIncludes bibliographic references and index.
520			_aIn 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.
650			_aPavement detection
650			_aDamage classification
650			_aGeolocation tracking
700			_aBondoc, Victor Sebastian D. _eauthor
700			_aPanis, Renato Jr. T. _eauthor
700			_aPastores, Efren Jr. D. _eauthor
700			_aRabe, Gary Clyde T. _eauthor
700			_aSilvano, Jevon A. _eauthor
942			_2lcc _cBTH COE _n0
999			_c30336 _d30336