朝向停车位占用识别：一种自监督方法
Toward Parking Spot Occupancy Recognition: A Self-Supervised Approach
摘要
随着城市区域的扩张，对停车场进行自动监控对于实现高效且可持续的城市发展至关重要。本研究提出了一种无需使用目标停车场的标注样本即可进行停车位占用识别的自监督方法。基于自监督迁移学习微调方案，该训练策略包含两个自监督阶段：首先使用未标注的通用数据进行训练，然后使用未标注的特定目标数据进行训练，最后仅使用通用停车场标签进行监督式微调。我们采用了SimCLR算法与ResNet-50编码器，并在三个公共数据集上进行了评估：PKLot、CNRPark-EXT和PLds。我们还提出了一种两阶段部署策略：首先部署一个通用模型，然后在部署后的前N天内收集到的未标注图像被纳入专用模型中进行自监督处理。实验结果表明，仅使用通用模型就能超越所有监督式及自监督基线模型，平均准确率可达97.2%；而采用两阶段策略后，这一数值可提升至97.8%。这些结果表明，自监督学习能够提供一种可扩展且节省标注资源的解决方案，适用于实际场景中的停车位占用监测需求。我们的训练模型和源代码可在https://github.com/LoanMaikon/Parking-Spot-Occupancy-Recognition上获取。
English Abstract
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.