PSL-YOLO11: Efficient and Accurate Parking Space Detection via a Dual Attention-Enhanced Lightweight Network

Existing parking space line detection systems face persistent challenges under complex environmental conditions, particularly lowlight conditions, occlusions, and textured background interference, which lead to compromised accuracy, excessive computational overhead and high latency. To address these limitations, this study proposes PSL-YOLO11, an enhanced lightweight detection framework based on the YOLO11 architecture. The innovation lies in a hybrid dynamic attention mechanism that synergistically combines the Convolutional Block Attention Module (CBAM) and the Global Attention Mechanism (GAM) for superior environmental adaptability. The framework implements multi-scale feature fusion with channel expansion for discriminative feature learning, and adopts GhostNet-based feature compression to reduce computational complexity. Additionally, a key point-driven geometric abstraction algorithm is designed to convert parking lines into quantifiable topological representations. Experimental results on the HNUST-IR dataset demonstrate that PSL-YOLO11 achieves 93.8% mAP and 96.15 FPS, with 22% fewer parameters than YOLO11 while maintaining real-time performance.