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

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.

End-to-End Trainable One-Stage Parking Slot Detection Integrating Global and Local Information

This paper proposes an end-to-end trainable one-stage parking slot detection method for around view monitor (AVM) images. The proposed method simultaneously acquires global information (entrance, type, and occupancy of parking slot) and local information (location and orientation of junction) by using a convolutional neural network (CNN), and integrates them to detect parking slots with their properties. This method divides an AVM image into a grid and performs a CNN-based feature extraction. For each cell of the grid, the global and local information of the parking slot is obtained by applying convolution filters to the extracted feature map. Final detection results are produced by integrating the global and local information of the parking slot through non-maximum suppression (NMS). Since the proposed method obtains most of the information of the parking slot using a fully convolutional network without a region proposal stage, it is an end-to-end trainable one-stage detector. In experiments, this method was quantitatively evaluated using the public dataset and outperforms previous methods by showing both recall and precision of 99.77%, type classification accuracy of 100%, and occupancy classification accuracy of 99.31% while processing 60 frames per second.