CNN-Based Two-Stage Parking Slot Detection Using Region-Specific Multi-Scale Feature Extraction

Although it is well-known that the two-stage approach outperforms one-stage in general object detection, they have similarly performed parking slot detection so far. We consider this because has not yet been adequately specialized for detection. Thus, paper proposes a highly detector uses region-specific multi-scale feature extraction. In first stage, proposed method finds entrance of as region proposal by estimating its center, length, and orientation. The second stage designates specific regions most contain desired information extracts features from them. That is, location orientation are separately extracted only locational orientational information. addition, multi-resolution maps utilized to increase both positioning classification accuracies. A high-resolution map used extract detailed (location orientation), while another low-resolution semantic (type occupancy). experiments, was quantitatively evaluated with two large-scale public datasets: SNU PS2.0 datasets. dataset, achieved state-of-the-art performance 95.75% recall 95.78% precision.