Attentional Graph Neural Network for Parking-Slot Detection

Abstract

Deep learning has recently demonstrated its promising performance for vision-based parking-slot detection. However, very few existing methods explicitly take into account learning the link information of the marking-points, resulting in complex post-processing and erroneous detection. In this paper, we propose an attentional graph neural network based parking-slot detection method, which refers the marking-points in an around-view image as graph-structured data and utilize graph neural network to aggregate the neighboring information between marking-points. Without any manually designed post-processing, the proposed method is end-to-end trainable. Extensive experiments have been conducted on public benchmark dataset, where the proposed method achieves state-of-the-art accuracy. Code is publicly available at https://github.com/Jiaolong/ gcn-parking-slot.

The Role of Deep Learning in Parking Space Identification and燩rediction燬ystems

“…Hence, parking identification and prediction systems have significant impacts on the efficiency of smart cities. DL has been achieving promising results in the identification and prediction of the availability of parking spaces [12][13][14][15][16][17][18], which makes significant impacts on smart cities.…”

“…A.4 Graph neural network (GNN) provides graphs to process data and perform node-, edge-, and graph-level prediction tasks [36]. GNN has been adopted in [15] to identify the availability of parking spaces. In [15], the GNN architecture consists of an input layer, two hidden layers, and an output layer, as shown in Fig.…”

“…GNN has been adopted in [15] to identify the availability of parking spaces. In [15], the GNN architecture consists of an input layer, two hidden layers, and an output layer, as shown in Fig. 6.…”

“…S.1 The identification system identifies and updates the availability of parking spaces in a realtime manner. For instance, the system identifies an available parking space when a vehicle arrives at a parking lot [12,15]. S.2 The prediction system predicts the availability of parking spaces.…”

“…The DL approach based on the GNN architecture (A.4) (see Fig. 6) has been applied in [15] to identify parking space availability (S.1). The GNN architecture consists of: (a) the input layer represents an input image of a parking lot (or parking spaces); (b) the hidden layer(s) have graphs and activation functions (e.g., ReLU); and (c) the output layer classifies objects, such as a car and an empty parking space.…”