Toward Parking Spot Occupancy Recognition: A Self-Supervised Approach

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self-supervised-learning parking-spot-recognition transfer-learning simclr resnet-50 fine-tuning computer-vision

Summary

This paper presents a self-supervised transfer learning approach for parking spot occupancy recognition that achieves high accuracy (up to 97.8%) with minimal labeled data using a two-stage training strategy with SimCLR and ResNet-50.

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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Source: https://huggingface.co/papers/2606.20886

Abstract

A self-supervised transfer learning approach for parking spot occupancy recognition that achieves high accuracy with minimal labeled data through two-stage training and deployment strategies.

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-supervisedtransfer learningfine-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 adoptSimCLRwith aResNet-50encoder and evaluate the method under aleave-one-out cross-environment protocolon three public datasets: PKLot, CNRPark-EXT, and PLds. We also introduce atwo-stage deployment strategyin which aStrong General Modelis initially deployed, followed by aSpecialized Modelthat incorporates unlabeled images collected during the first N days of deployment in a self-supervised manner. Experimental results show that theStrong General Modelalone 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 thatself-supervised learningenables 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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