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Deep Learning-Based Road Lane Quality Assessment from Egocentric Dashcam Video

Shaif Chowdhury, Akhila Yeruvasu, Moshiur Rahman Tonmoy, Ayush Goyal, Dunren Che · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28508 · cited by 0 works (OpenAlex) · open-access PDF

Russian

Удостоверяемые отметки о ранжировании PSIS в базовых сегментах имеют важное значение как для водителей, так и для автоматизированных систем вождения, но их состояние все еще обычно оценивается с помощью ручной инспекции. Существующие автономные наборы данных в первую очередь поддерживают обнаружение полосы и понимание сцены и обычно не имеют ориентированных на техническое обслуживание этикеток для маркировки полосы, непрерывности или деградации. В этом исследовании мы представляем набор данных с помощью смартфона и базовую базу для оценки качества маркировки полосы. Пилотный набор данных содержит 63,839 дорожных рамок, собранных по всему Южному Техасу с использованием приложения dashcam с поддержкой GPS. Подгруппа из 3,339 кадров соответствует отчетам OpenStreetM

Machine translation (Meta M2M100); the English original follows.

Abstract

Reliable lane markings are important for both human drivers and automated driving systems, but their condition is still commonly assessed through manual inspection. Existing autonomous-driving datasets primarily support lane detection and scene understanding and usually lack maintenance-oriented labels for lane-marking visibility, continuity, or degradation. In this study, we present a geo-referenced smartphone dashcam dataset and baseline framework for lane-marking quality assessment. The dataset contains 63,839 road-facing frames collected across South Texas using a GPS-enabled dashcam application. A subset of 3,339 frames is matched with OpenStreetMap-derived lane-count labels, which are used as weak supervision for lane-count regression and map-consistency evaluation. We propose three lane-quality measures: Contrast Stability Index (CSI), which captures lane-background contrast using Laplacian responses; Line Continuity Ratio (LCR), which estimates structural continuity using edge and Hough-line evidence; and Regression Lane Confidence Score (RLCS), which measures agreement between predicted and map-derived lane counts on matched frames. The baseline prediction models attained strong performance, with CSI (MAE $0.039$, $R^2=0.97$) and LCR (MAE $0.093$, $R^2=0.84$). Notably, CSI and LCR are no-reference, segmentation-derived quality metrics that can be computed without dense human labels. In a pilot validation using 50 human-ranked images, these proposed lane-specific metrics show stronger rank agreement with human lane-quality ordering (e.g., Spearman's $ρ=0.7316$ and Kendall's $τ=0.5472$ for LCR) than general image-quality measures such as PSNR, SSIM, BRISQUE, NIQE, and LPIPS. We believe the dataset, metrics, and baseline provide an initial step toward scalable, low-cost, location-aware lane-marking quality assessment. Data and code: https://github.com/Shaif95/Road_Quality

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Topics: Baseline (sea), Computer science, Artificial intelligence, Regression, Contrast (vision), Quality (philosophy)

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