Smarter Parking Systems for Rainy Days

Smart parking systems often struggle with accuracy during heavy rainfall due to degraded image quality, which affects the ability to detect available parking spaces. This paper introduces image preprocessing techniques, including brightness adjustment, contrast enhancement, and noise reduction, to improve image clarity under adverse weather conditions. These techniques were applied to the PKLot dataset, where the results demonstrated significant improvements in both image quality and car detection accuracy. Performance increased from 84.28% to 95.71 %, surpassing existing methods. By addressing the limitations caused by poor visibility during rainy weather, this approach enhances the reliability and efficiency of smart parking systems, ensuring improved parking management in challenging conditions.