面向停车位占用识别的自监督方法

摘要

随着城市区域不断扩张，停车场自动监测对于建设高效可持续的城市至关重要。本文提出一种无需目标停车场标注样本的自监督停车位占用识别方法。基于自监督迁移学习微调框架，所提出的训练策略包含两个自监督阶段：首先在无标注通用数据上预训练，随后在无标注目标特定数据上继续训练，最后仅使用通用停车场标签进行监督微调。我们采用搭载ResNet-50编码器的SimCLR模型，并在三个公开数据集（PKLot、CNRPark-EXT和PLds）上采用留一环境交叉验证协议进行评估。同时提出两阶段部署策略：初始部署强通用模型，随后结合部署前N天收集的无标注图像，以自监督方式构建专业模型。实验表明，仅强通用模型即可超越监督与自监督基线方法，达到97.2%的平均准确率；采用所提两阶段策略后，准确率进一步提升至97.8%。这些结果证明，自监督学习能够为现实停车场占用监测提供可扩展且低标注成本的解决方案。我们训练的模型及源代码已在 https://github.com/LoanMaikon/Parking-Spot-Occupancy-Recognition 公开。

English

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.