Detection of Road Surface Ice and Snow Cover Using the HPC-D Segmentation Algorithm Combined with the SCI Illumination Enhancement Model

Counts differ because each database indexes a different set of publications. We treat OpenAlex as the canonical count; Google Scholar is not shown (no API, and crawling it violates its ToS).

Road-surface ice, snow and standing water can impair winter driving, while irregular boundaries and changing illumination complicate image-based assessment. This study investigates pixel-wise semantic segmentation using HPC-D, a DeepLabv3+ architecture incorporating convolutional block attention (CBAM), parallel axial attention (PAA) and hierarchical feature fusion (HFF), together with self-calibrated illumination (SCI) enhancement for low-light images. The reported dataset contains 5000 image entries from a campus road environment, with a reported training/validation/test allocation of 3000/1000/1000. The original experimental tables report a mean intersection-over-union (mIoU) of 0.96, a reported mean average precision (mAP) of 0.95 and an accuracy of 0.98 for HPC-D. Relative to the other rows in the segmentation comparison, the numerical differences are 2–21 percentage points in mIoU, 0–10 percentage points in mAP and 3–14 percentage points in accuracy. In the illumination comparison, the SCI row exceeds the no-enhancement row by 16 percentage points in mIoU and 17 percentage points in mAP. These are descriptive differences in the reported results. Incomplete documentation of image lineage, acquisition-session separation, metric implementation and comparator configurations limits their interpretation as evidence of independent generalization. The contribution is therefore an architectural integration and a preliminary evaluation for visually annotated road-surface segmentation; cross-camera robustness, shadow rejection and real-time vehicle operation remain unvalidated.