LCRM: A Linear-Camera Dataset for Road Marking Segmentation and Damage Evaluation | Zendy

Ilya Makarov | Zendy; Nikita Sidelnikov | Zendy; Daniil Storonkin | Zendy; Eugene Kapustin | Zendy; Alexander Karandeev | Zendy; Alexey Maslov | Zendy; Maksim Golyadkin | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

LCRM: A Linear-Camera Dataset for Road Marking Segmentation and Damage Evaluation

Author(s) -

Ilya Makarov,

Nikita Sidelnikov,

Daniil Storonkin,

Eugene Kapustin,

Alexander Karandeev,

Alexey Maslov,

Maksim Golyadkin

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3612491

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

We introduce the Linear-Camera Road Marking (LCRM) dataset, which is the first to couple segmentation with marking damage evaluation on the same images, and the first captured with a linear (line-scan) camera . LCRM spans Russian cities, covers 12 marking types, and grades instance damage with a 3-level scheme aligned with maintenance thresholds. We release 3,966 labeled images (train/test) and 18,847 unlabeled images. We also benchmark representative models across four tasks (binary and multi-class semantic segmentation, instance segmentation, and damage evaluation) to establish baselines for this new sensing regime.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research