Smart Parking Space Detection Using Advanced Deep Learning Techniques

Lalu Heri Aguswandi, Bambang Krismono Triwijoyo, Galih Hendro Martono

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This study aims to develop an accurate and efficient empty parking slot detection model to assist users in finding parking spaces. The developed model utilizes YOLOv11 as a pretrained model and demonstrates excellent performance with a precision of 99%, recall of 99%, and a Mean Average Precision (mAP) of 99%. These results validate the model's ability to accurately detect empty parking slots with 100 training epochs. Additionally, the model operates in real-time with a frame rate of 25 frames per second (FPS)

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DOI: http://dx.doi.org/10.30811/jaise.v5i1.6473

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