Deep Learning Based Parking Slot Detection and Tracking: PSDT-Net

Park, Younghyo; Ahn, Joonwoo; Park, Jaeheung

doi:10.1007/978-3-030-97672-9_26

Cited by 2 publications

(1 citation statement)

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“…However, by introducing more detailed and informative junction representations with corresponding combining rules, they avoid using the additional verification stage as in the method in [18]. The method in [21] detects all four corners of a parking slot and combines them in the second stage using geometric rules. This paper also proposes the auxiliary junction in case junctions are occluded.…”

Section: Related Workmentioning

confidence: 99%

Transformer-Based Parking Slot Detection Using Fixed Anchor Points

Bui,

Suhr

2023

IEEE Access

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Transformer-based architectures have recently gained significant attention in various computer vision tasks. Their ability to capture non-local dependencies and intricate characteristics makes them a promising complement to CNNs. However, their application in parking slot detection tasks is still limited. Thus, this paper proposes an appropriate way to apply transformer-based architectures to parking slot detection tasks. The proposed method adopts the Detection Transformer (DETR) architecture, which employs a standard transformer encoder-decoder framework. Since this approach requires a long training time, this paper suggests utilizing fixed anchor points to replace object queries in the original DETR architecture. Each anchor point is assigned a known location and focuses only on a predefined area of the feature map, resulting in a considerable reduction in training time. In addition, this paper suggests using a more suitable and efficient two-point parking slot representation to improve detection performance. In experiments, the proposed method was evaluated with the public large-scale SNU dataset and showed comparable detection performance to the state-of-the-art CNN-based methods with 96.11% recall and 96.61% precision.INDEX TERMS Automatic parking system, parking slot detection, deep learning, transformers, convolutional neural network (CNN), around view monitor (AVM).

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Section: Related Workmentioning

confidence: 99%