# 停车位占用识别的自监督方法

- 来源：HuggingFace Daily Papers（社区热门论文）
- 发布时间：2026-06-18 08:00
- AIHOT 分数：41
- AIHOT 链接：https://aihot.virxact.com/items/cmqqrfwu40cpvslp51rgod6r8
- 原文链接：https://arxiv.org/abs/2606.20886

## AI 摘要

提出一种无需目标停车场标注样本的自监督占用识别方法。训练策略包含两个自监督阶段（先在未标注通用数据上预训练，再在未标注目标数据上微调），然后仅在通用停车场标签上监督微调。采用SimCLR与ResNet-50编码器，在PKLot、CNRPark-EXT和PLds三个数据集上通过留一法交叉环境评估。还引入两阶段部署策略：先部署强通用模型，再结合部署前N天收集的未标注图像自监督训练专用模型。强通用模型平均准确率97.2%，两阶段策略提升至97.8%。模型和代码已开源。

## 正文

As urban areas expand, automatic monitoring of parking lots becomes essential for efficient and sustainable cities. This work proposes a self-supervised approach for parking spot occupancy recognition that requires no labeled samples from the target parking lot. Building upon a self-supervised transfer learning fine-tuning protocol, the proposed training strategy consists of two self-supervised stages: first on unlabeled generic data and then on unlabeled target-specific data, followed by supervised fine-tuning using only generic parking lot labels. We adopt SimCLR with a ResNet-50 encoder and evaluate the method under a leave-one-out cross-environment protocol on three public datasets: PKLot, CNRPark-EXT, and PLds. We also introduce a two-stage deployment strategy in which a Strong General Model is initially deployed, followed by a Specialized Model that incorporates unlabeled images collected during the first N days of deployment in a self-supervised manner. Experimental results show that the Strong General Model alone outperforms supervised and self-supervised baselines, achieving an average accuracy of 97.2%, which further improves to 97.8% with the proposed two-stage strategy. These results demonstrate that self-supervised learning enables a scalable and labelefficient solution for real-world parking occupancy monitoring. Our trained models and source code are publicly available at https://github.com/LoanMaikon/Parking-Spot-Occupancy-Recognition.