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