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This study investigates diffusion-based synthetic data augmentation to improve vehicle detection under adverse weather and lighting conditions. It examines reference image quality and mAP-guided data allocation to address class imbalance and improve detection robustness.
This study investigates the use of recycled plastic waste as an additive in hot mix asphalt concrete for road construction. The research team developed mixture proportions and conducted comprehensive laboratory and field performance evaluations in accordance with the standards of the Department of Highways, Thailand.
This research studies and develops a prototype device for measuring Mean Profile Depth (MPD), representing pavement macro-texture, as supporting data for pavement friction evaluation and road safety management.
This study develops and evaluates an AHP-TOPSIS framework for prioritizing low-volume road maintenance under budget constraints. The approach integrates multiple criteria, including pavement performance, accessibility, and regional distribution, to support more balanced budget allocation.
This research presents a computer vision-based approach for road pavement damage detection and assessment using a monocular camera mounted on a vehicle. The proposed system integrates the YOLO9tr object detection model with image-based depth estimation to detect damage types and estimate their dimensions in 3D.
This research examines the potential and strength of local administrative organizations in the local roadway field, focusing on their capacity to undertake local roadway-related operations.
This research presents YOL026-RD, a network for detecting road surface damage from survey imagery. It incorporates Learnable Contrast Enhancement and Edge-Guided Downsampling to improve the detection of small-scale damage and preserve image details during processing.
This study presents road asset detection in Thai highway panorama images using Transformer-Based YOLOX with a Feature Pyramid Network (FPN) Decoder, focusing on kilometer signs and kilometer stones. The model achieved 61.5 AP, outperforming YOLOv5L by 2.56% AP on the test-dev dataset.
iNFRA Co., Ltd. is proud to announce that its article, “A New Era of Traffic Data Collection with Artificial Intelligence,” has been featured in Civil Insight Series Content (Ep.46 DIT-CACE/004) by the Civil Engineering Committee of the Engineering Institute of Thailand under H.M. The King’s Patronage (EIT). This recognition reflects the company’s expertise in applying Artificial Intelligence (AI) to support traffic engineering and infrastructure development.
Infra Plus Co., Ltd. (iNFRA) has been officially approved as a Corporate Member of the Council of Engineers Association of Thailand (CEAT).
iNFRA participated in the 2025 Department of Highways Knowledge and Innovation Exhibition, where we had the opportunity to showcase our equipment and various system platforms at our demonstration booth.
iNFRA is delighted to announce that we have officially received the ISO 29110 certification On behalf of Infra Plus Co., Ltd.
Infra Plus Co., Ltd., supported by the Electricity Generating Authority of Thailand (EGAT), Mae Moh Power Plant, is conducting research for the project titled, “The Contracted Project for Installation of Ice Thermal Storage System Combined with Solar Energy for Energy Management to Cool the Near-Zero Carbon Building within The Blocks at Mae Moh Power Plant.
On July 23, 2024, Relief and Community Health Bureau (RCHB), Thai Red Cross Society participate in project “Village Health Volunteers all over Thailand joined together to pinpoint location through the “Phonphai” application for vulnerable groups in honor of His Majesty the King, on the ceremony of the auspicious occasion of His Majesty the King’s 6th Cycle Birthday Anniversary, 28 July 2024″