Toward Parking Spot Occupancy Recognition: A Self-Supervised Approach

• URL: http://arxiv.org/abs/2606.20886v1
• Date: Thu, 18 Jun 2026 19:27:18 GMT
• Title: Toward Parking Spot Occupancy Recognition: A Self-Supervised Approach
• Abstract summary: Self-supervised approach for parking spot occupancy recognition requires no labeled samples from the target parking lot.<n>We adopt SimCLR with a ResNet-50 encoder and evaluate the method under a leave-one-out cross-environment protocol.<n> Experimental results show that the Strong General Model alone outperforms supervised and self-supervised baselines.
• Score: 0.8220217498103312
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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