KRID: A Large-Scale Nationwide Korean Road Infrastructure Dataset for Comprehensive Road Facility Recognition

Hyeongbok Kim (), Eunbi Kim, Sanghoon Ahn, Beomjin Kim, Sung Jin Kim, Tae Kyung Sung, Lingling Zhao, Xiaohong Su and Gilmu Dong ()
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Hyeongbok Kim: Testworks, Inc., Seoul 01000, Republic of Korea
Eunbi Kim: Testworks, Inc., Seoul 01000, Republic of Korea
Sanghoon Ahn: Testworks, Inc., Seoul 01000, Republic of Korea
Beomjin Kim: Testworks, Inc., Seoul 01000, Republic of Korea
Sung Jin Kim: Korea Automotive Technology Institute (KATECH), Cheonan-si 31000, Republic of Korea
Tae Kyung Sung: WiFive Ltd., Daejeon 34000, Republic of Korea
Lingling Zhao: Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
Xiaohong Su: Faculty of Computing, Harbin Institute of Technology, Harbin 150001, China
Gilmu Dong: Testworks, Inc., Seoul 01000, Republic of Korea

Data, 2025, vol. 10, issue 3, 1-28

Abstract: Comprehensive datasets are crucial for developing advanced AI solutions in road infrastructure, yet most existing resources focus narrowly on vehicles or a limited set of object categories. To address this gap, we introduce the Korean Road Infrastructure Dataset (KRID), a large-scale dataset designed for real-world road maintenance and safety applications. Our dataset covers highways, national roads, and local roads in both city and non-city areas, comprising 34 distinct types of road infrastructure—from common elements (e.g., traffic signals, gaze-directed poles) to specialized structures (e.g., tunnels, guardrails). Each instance is annotated with either bounding boxes or polygon segmentation masks under stringent quality control and privacy protocols. To demonstrate the utility of this resource, we conducted object detection and segmentation experiments using YOLO-based models, focusing on guardrail damage detection and traffic sign recognition. Preliminary results confirm its suitability for complex, safety-critical scenarios in intelligent transportation systems. Our main contributions include: (1) a broader range of infrastructure classes than conventional “driving perception” datasets, (2) high-resolution, privacy-compliant annotations across diverse road conditions, and (3) open-access availability through AI Hub and GitHub. By highlighting critical yet often overlooked infrastructure elements, this dataset paves the way for AI-driven maintenance workflows, hazard detection, and further innovations in road safety.

Keywords: road infrastructure; object detection; image segmentation; data annotation; intelligence driving (search for similar items in EconPapers)
JEL-codes: C8 C80 C81 C82 C83 (search for similar items in EconPapers)
Date: 2025
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