SlotNet: A Lightweight Network with Skeleton-Driven and Adaptive Completion for Robust Detection of Degraded Parking Slot Lines

In response to degraded parking slot markings caused by wear and tear, water accumulation, or occlusion, which significantly impair the perception accuracy and localization robustness of automated parking systems, this paper proposes SlotNet, a lightweight enhancement network. The proposed method incorporates a skeleton-driven adaptive width completion algorithm to mitigate segmentation errors and restore the topological continuity of fractured parking slot lines. The network integrates three lightweight modules: Lightweight Reparameterized VGG (LightRepVGG) for enhancing the extraction of fine-grained structural features via structural reparameterization, Parallel Perceptual Structured Attention—Light (PASA_Light) for multi-scale feature fusion, and Adaptive Decoupled Detect and Segment (AdaDecDS) for anchor-free decoupled detection and segmentation. The experimental results show that SlotNet achieves an inference speed of 65.75 Frames Per Second (FPS). The mask average precision (mask mAP@0.5) reaches 90.2% under an Intersection over Union (IoU) threshold of 0.5, enabling robust completion and accurate detection of degraded parking slot lines. Compared with existing detection, SlotNet achieves a superior balance among accuracy, robustness, and real-time performance, making it suitable for deployment on embedded in-vehicle platforms.