Parking slot detection is an essential yet challenging task in the field of self-driving perception. During parking, vehicles often block part of the parking slots which makes the corners occluded. In addition, due to the impact of the external environment, the corners of the parking slot may be blurred. Existing parking slot detection algorithms based on parking slot markings are sensitive to the corners of the parking slots, which makes it difficult to cope with the above scenario. To address this problem, we propose a parking slot entrance line detection algorithm called RPSED, which is the first to apply rotating object detection to the parking slot entrance line. RPSED takes a different route from traditional corner detection methods by focusing on the entrance lines of parking slots to grasp the intricate geometric details inherent to parking slots, which solves the problem that existing parking slot detection algorithms cannot detect parking slots with blurred corners. To further improve the precision and recall of the model and make the model more generalizable, we propose a model ensemble strategy to match and select the results of multiple models. Moreover, we propose two manually optimized parking slot dataset named RPS2.0 and RPSV, which adds more annotations with obstructed corners or obscured configurations to the datasets ps2.0 and psv, making the model evaluation more reasonable and realistic. Experimental results on the RPS2.0 and RPSV benchmarks demonstrate the superiority of our approach compared to existing state-of-the-art methods.

Despite the recent success of Graph Neural Networks (GNNs), their learning pipeline is guided only by the input graph and the desired output of certain tasks, failing to capture useful patterns when not enough data are presented. Existing attempts incorporate auxiliary knowledge to mitigate this issue, most of which are not in a unified structure or hard to obtain. Noticing that nodes in graphs usually form implicit hierarchical structures, we proposed to integrate category taxonomies into the learning process of GNNs. A category taxonomy is a form of domain knowledge with a hierarchical tree structure, which is widely adopted in real-world scenarios. In this paper, we introduce Taxonomy-Enhanced Graph Neural Networks (Taxo-GNN). Specifically, we jointly optimize the taxonomy representation and node representation tasks, where categories in taxonomy are mapped to Gaussian distributions and nodes are embedded with the GNN framework. To characterize the bidirectional interaction between the taxonomy and the graph, the model is comprised of two modules, namely information distillation for taxonomy and knowledge fusion to graph. Information is first distilled from the graph and aligned with the hierarchical structure of the taxonomy in a bottom-to-top mechanism.After that, knowledge brought by the taxonomy is in turn fused to the graph convolution process, in the form of taxonomy-aware aggregation weights and taxonomy-augmented contexts. Extensive experiments on real-world datasets in multiple downstream tasks verify the effectiveness of our model.