Road maintenance and computer vision
Center Lead
Research connects road-maintenance prioritization, budget allocation and computer vision for pavement damage assessment.
Listed separately from selected publications. Record type and available evidence are stated for each item.
2010 · Master’s thesis · Chulalongkorn University
Develops models for estimating skin and deep patching quantities using pavement age, traffic, pavement structure and rainfall. Separate thesis record underlying the existing journal work; DOI 10.58837/CHULA.THE.2010.1514.
2013 · Conference bibliography · Chulalongkorn University
Conference work on improving the Department of Rural Roads central database. Listed in the coauthor’s Chulalongkorn University bibliography; full proceedings text has not been verified.
2013 · Conference bibliography · Chulalongkorn University
Conference work on improving maintenance management for local roads. Listed in the coauthor’s Chulalongkorn University bibliography; full proceedings text has not been verified.
2014 · Conference bibliography · Chulalongkorn University
Conference work on location-based services for road maintenance, listed in a coauthor’s university bibliography for NCCE 19 in Khon Kaen. The full proceedings paper has not been verified.
Published research, with a short guide to the question and method.
9 selected publications
Suranaree Journal of Science and Technology, 33(4), 010450 (1–15)
Evaluates an AHP–TOPSIS framework for rural-road maintenance prioritization using 724 one-kilometer road sections across eight regional bureaus in Thailand. Compared with conventional allocation under the same budget, the study reports 7.4% greater maintenance coverage and lower regional variation in post-maintenance roughness.
arXiv:2608.15713v2 · Preprint · Revised 18 August 2026
A pavement-distress detector informed by an audit of road-survey imagery. LearnableContrast adjusts local contrast, while EdgeSPD uses edge information during downsampling. The study compares five model scales with matched baselines; its findings are reported in an arXiv preprint.
IOP Conference Series: Earth and Environmental Science, 1450(1), 012004
Combines AHP factor weighting with TOPSIS ranking to prioritize road maintenance. Economic, engineering, environmental and social criteria help account for low-volume roads that traffic and roughness measures alone may overlook. The study evaluates the decision-support approach on a Thai rural-road network.
2024 International Conference on Intelligent Computing and Next Generation Networks (ICNGN), 1–5
Conference paper combining YOLO9tr and depth estimation for detecting road damage and analysing its size. Bibliographic details follow IEEE’s Crossref deposit.
The 3rd International Conference on Advance Civil Engineering and Smart Structure · Author-uploaded conference manuscript, November 2024
Combines YOLO9tr damage detection with calibrated monocular depth estimation to assess pavement damage using a vehicle-mounted camera. The conference manuscript investigates both damage classification and dimensional measurement, extending the detector toward road-maintenance assessment.
Journal of Real-Time Image Processing, 21, Article 163
A lightweight pavement-damage detector based on YOLOv9 with partial attention. The study evaluates accuracy and inference speed using road images from multiple countries and an expanded set of damage classes. The journal article and its earlier arXiv version are grouped as one work.
International Journal of GEOMATE, 22(91), 70–79
Uses the Analytic Hierarchy Process to weight economic, engineering and social factors for rural-road maintenance. It relates priorities to the Department of Rural Roads’ strategic objectives, including network connectivity and the needs of low-traffic routes.
Journal of Transportation and Logistics (TRANSLOG), Vol. 2020, Issue 1, Article 7, 122–143
Assesses local-road management capacity across 442 local administrative organisations in Thailand. It examines road registration, condition and safety assessment, knowledge use, and maintenance equipment, identifying differences in capacity across types of local authority.
Thai-language article. English title follows the DOI metadata; author names are transliterated for this page. The source record preserves the original Thai byline.
Engineering Journal of Research and Development, 21(4), 57–64
Models pavement patching quantities using road age, traffic and structural characteristics to support maintenance budgeting.
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Selected research outputs follow the original sources, including work at different institutions. Not every item is a current center project.
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