Occlusion-Aware Lane Reconstruction and Lane–Vehicle Mask-Space Reasoning for Oversize-Vehicle Violation Detection

Background: Oversize transport vehicles pose substantial challenges to vision-based traffic monitoring because their large bodies can obscure lane markings and wheel–ground contact regions, while elevated oblique camera views introduce strong perspective distortion. To address these difficulties, this study presents an occlusion-aware lane reconstruction and lane–vehicle mask-space reasoning framework for oversize-vehicle violation detection. The lane-perception branch, termed CLRNet-ALD, extends CLRNet through adaptive cross-layer feature fusion, large-kernel attention, deformable convolution, and multi-loss supervision to improve the reconstruction of partially or severely occluded lane boundaries. In parallel, a YOLOv12-based instance-segmentation branch extracts vehicle contours and instance masks. The lane and vehicle masks are subsequently aligned in a common image coordinate system, and interpretable geometric rules based on row-matched boundary comparison, instance counting, and mask overlap are used to identify designated-lane deviation, simultaneous passage of multiple oversize vehicles, and lane-line crossing. On a 4000-image lane-focused dataset, CLRNet-ALD increased precision, recall, F1-score, and IoU from 86.3%, 83.9%, 85.1%, and 71.4% to 90.2%, 88.5%, 89.3%, and 77.8%, respectively. In a separate 600-image system-level evaluation, 157 of 167 confirmed violation cases were correctly recovered, corresponding to a positive-case recovery rate of 94.0%, while 141 of 143 confirmed multi-type vehicle cases were correctly identified (98.6%). These results demonstrate the practical feasibility of combining occlusion-aware lane reconstruction with interpretable lane–vehicle spatial reasoning. Because the lane-component and system-level experiments were conducted at different development stages, the reported system-level results are interpreted as validation of the complete reasoning pipeline rather than as a controlled measurement of the end-to-end improvement attributable specifically to CLRNet-ALD.