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

arXiv: https://arxiv.org/abs/2509.13133 · HTML: https://mlsanity.com/paper/2509.13133/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 46 references that are in the map.
- First posted on arXiv 16 Sep 2025.

## Builds on (most cited first, 17 of 17)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [YOLO9000: Better, Faster, Stronger](https://mlsanity.com/paper/1612.08242/) (2016)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning](https://mlsanity.com/paper/1704.03976/) (2017)
- [Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning](https://mlsanity.com/paper/1908.02983/) (2019)
- [Perturbed and Strict Mean Teachers for Semi-supervised Semantic Segmentation](https://mlsanity.com/paper/2111.12903/) (2021)
- [Attentional Graph Neural Network for Parking-slot Detection](https://mlsanity.com/paper/2104.02576/) (2021)
- [VH-HFCN based Parking Slot and Lane Markings Segmentation on Panoramic Surround View](https://mlsanity.com/paper/1804.07027/) (2018)
- [End-to-End Trainable One-Stage Parking Slot Detection Integrating Global and Local Information](https://mlsanity.com/paper/2003.02445/) (2020)
- [Enhanced Parking Perception by Multi-Task Fisheye Cross-view Transformers](https://mlsanity.com/paper/2408.12575/) (2024)
- [DFNet: Semantic Segmentation on Panoramic Images with Dynamic Loss Weights and Residual Fusion Block](https://mlsanity.com/paper/1806.07226/) (2018)
- [SPFCN: Select and Prune the Fully Convolutional Networks for Real-time Parking Slot Detection](https://mlsanity.com/paper/2003.11337/) (2020)
- [CNN-based Two-Stage Parking Slot Detection Using Region-Specific Multi-Scale Feature Extraction](https://mlsanity.com/paper/2108.06185/) (2021)

## Abstract (arXiv)

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.