  Autonomous parking systems start with the detection of available parking
slots. Parking slot detection performance has been dramatically improved by
deep learning techniques. Deep learning-based object detection methods can be
categorized into one-stage and two-stage approaches. Although it is well-known
that the two-stage approach outperforms the one-stage approach in general
object detection, they have performed similarly in parking slot detection so
far. We consider this is because the two-stage approach has not yet been
adequately specialized for parking slot detection. Thus, this paper proposes a
highly specialized two-stage parking slot detector that uses region-specific
multi-scale feature extraction. In the first stage, the proposed method finds
the entrance of the parking slot as a region proposal by estimating its center,
length, and orientation. The second stage of this method designates specific
regions that most contain the desired information and extracts features from
them. That is, features for the location and orientation are separately
extracted from only the specific regions that most contain the locational and
orientational information. In addition, multi-resolution feature maps are
utilized to increase both positioning and classification accuracies. A
high-resolution feature map is used to extract detailed information (location
and orientation), while another low-resolution feature map is used to extract
semantic information (type and occupancy). In experiments, the proposed method
was quantitatively evaluated with two large-scale public parking slot detection
datasets and outperformed previous methods, including both one-stage and
two-stage approaches.
