面向停車位佔用識別：一種自監督方法

摘要

隨著都市區域不斷擴張，停車場的自動監控對於永續城市的效率與管理至關重要。本研究提出一種無需目標停車場標註樣本的自監督式停車位占用辨識方法。基於自監督遷移學習微調架構，所提出的訓練策略包含兩個自監督階段：首先在無標註的一般資料上進行訓練，其次在無標註的特定目標資料上進行訓練，最後僅使用一般停車場的標籤進行監督式微調。我們採用結合ResNet-50編碼器的SimCLR，並在三個公開資料集（PKLot、CNRPark-EXT及PLds）上，以留一交叉環境評估方法進行驗證。此外，我們提出兩階段部署策略：先部署強通用模型，接著利用部署前N天以自監督方式收集的無標註影像，部署專用模型。實驗結果顯示，僅使用強通用模型即超越監督式與自監督式基準方法，平均準確率達97.2%，而採用兩階段策略後進一步提升至97.8%。這些結果證明，自監督學習能為即時停車占用監控提供可擴展且節省標註成本的解決方案。我們訓練好的模型與原始碼已公開於 https://github.com/LoanMaikon/Parking-Spot-Occupancy-Recognition。

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.