Advancing Real-World Parking Slot Detection with Large-Scale Dataset and Semi-Supervised Baseline

• URL: http://arxiv.org/abs/2509.13133v1
• Date: Tue, 16 Sep 2025 14:50:19 GMT
• Title: Advancing Real-World Parking Slot Detection with Large-Scale Dataset and Semi-Supervised Baseline
• Authors: Zhihao Zhang, Chunyu Lin, Lang Nie, Jiyuan Wang, Yao Zhao,
• Abstract summary: This study focuses on parking slot detection using surround-view cameras, which offer a comprehensive bird's-eye view of the parking environment.<n>We first construct a large-scale parking slot detection dataset (CRPS-D), which includes various lighting distributions, diverse weather conditions, and challenging parking slot variants.<n>We develop a semi-supervised baseline for parking slot detection, termed SS-PSD, to further improve performance by exploiting unlabeled data.
• Score: 65.25540269603553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As automatic parking systems evolve, the accurate detection of parking slots has become increasingly critical. This study focuses on parking slot detection using surround-view cameras, which offer a comprehensive bird's-eye view of the parking environment. However, the current datasets are limited in scale, and the scenes they contain are seldom disrupted by real-world noise (e.g., light, occlusion, etc.). Moreover, manual data annotation is prone to errors and omissions due to the complexity of real-world conditions, significantly increasing the cost of annotating large-scale datasets. To address these issues, we first construct a large-scale parking slot detection dataset (named CRPS-D), which includes various lighting distributions, diverse weather conditions, and challenging parking slot variants. Compared with existing datasets, the proposed dataset boasts the largest data scale and consists of a higher density of parking slots, particularly featuring more slanted parking slots. Additionally, we develop a semi-supervised baseline for parking slot detection, termed SS-PSD, to further improve performance by exploiting unlabeled data. To our knowledge, this is the first semi-supervised approach in parking slot detection, which is built on the teacher-student model with confidence-guided mask consistency and adaptive feature perturbation. Experimental results demonstrate the superiority of SS-PSD over the existing state-of-the-art (SoTA) solutions on both the proposed dataset and the existing dataset. Particularly, the more unlabeled data there is, the more significant the gains brought by our semi-supervised scheme. The relevant source codes and the dataset have been made publicly available at https://github.com/zzh362/CRPS-D.

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