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Image-Based Parking Space Occupancy Classification: Dataset and Baseline
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We introduce a new dataset for image-based parking space occupancy classification: ACPDS. Unlike in prior datasets, each image is taken from a unique view, systematically annotated, and the parking lots in the train, validation, and test sets are unique. We use this dataset to propose a simple baseline model for parking space occupancy classification, which achieves 98% accuracy on unseen parking lots, significantly outperforming existing models. We share our dataset, code, and trained models under the MIT license.
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Smart Parking with Pixel-Wise ROI Selection for Vehicle Detection Using YOLOv8, YOLOv9, YOLOv10, and YOLOv11
A post-processing pixel-wise ROI mask applied to pretrained YOLO detections improved parking occupancy counting, with YOLOv9e reaching 99.68% balanced accuracy on a custom dataset, though the evaluation metrics are no...
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