cs.CVOct 20, 2026

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

Authors: Shaif Chowdhury, Akhila Yeruvasu, Moshiur Rahman Tonmoy, Ayush Goyal, Dunren Che

Organizations: Department of Electrical Engineering and Computer Science, Texas A&M University–Kingsville, Kingsville, TX 78363, USA

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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 0.0390.039, R2=0.97R^2=0.97) and LCR (MAE 0.0930.093, R2=0.84R^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=0.7316ρ=0.7316 and Kendall's τ=0.5472τ=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

Figures & tables

Figure 1: Example frame from our smartphone dashcam dataset, captured near Kingsville, Texas, USA. The image includes a metadata overlay generated by the GPS Map Camera application, which automatically embeds location-based information such as latitude (27.55995°), longitude (-97.87835°), and physical address (3421 FM1355, Kingsville, TX 78363, USA). It also records the capture time (26 August 2025, 03:13 PM GMT–05:00), providing spatiotemporal context for each frame. This overlay enables direct mapping between visual lane conditions and real-world geospatial coordinates, facilitating integration with external mapping APIs and supporting large-scale, location-aware evaluation of road and lane quality.
Figure 2: Representative smartphone dashcam scenes showing diverse roadway and lane-marking conditions used for lane quality assessment.
Figure 3: Examples of YOLOP-segmented lane-line images, covering one- and two-lane road scenarios.
Figure 4: Residual distribution for lane-count regression. Most predictions cluster near zero error, with tails caused by segmentation noise, occlusions, and mapping failures.
Figure 5: Distribution of predicted CSI percentiles on the MatchedFrames set. Higher percentiles indicate stronger and more stable lane–background contrast, while lower values correspond to faded markings, glare, or poor illumination.
Figure 6: Distribution of predicted LCR percentiles on the MatchedFrames set. Higher percentiles correspond to long, continuous lane markings, whereas lower values indicate fragmented or discontinuous lane structures.
Figure 7: Ten example images manually ranked from the highest lane quality (left) to the lowest lane quality (right). These images are used to validate the ranking consistency of the proposed CSI and LCR metrics against human assessment.
MethodSpearman’s ρ\rhoKendall’s τ\tau
LCR0.73160.5472
CSI0.61120.4400
NIQE-0.5699-0.3959
LPIPS-0.5251-0.3878
SSIM-0.4504-0.3469
PSNR-0.2526-0.1804
Table I: Ranking agreement with manually assigned lane-quality rankings. Spearman’s ρ\rho and Kendall’s τ\tau measure agreement with the human ranking. Positive correlations indicate direct agreement, while negative correlations are expected for BRISQUE, NIQE, and LPIPS because lower scores indicate better image quality. The proposed LCR achieves the strongest agreement with human perception, followed by CSI , outperforming all conventional image quality metrics.
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