Parking Space Detection in the City of Granada

TL;DR

This study develops a neural network-based method using aerial imagery and semantic segmentation to detect parking spaces and vehicles in Granada, enhancing urban parking management and planning.

Contribution

Introduces a novel Granada-specific dataset and compares multiple neural network models for urban parking space detection using aerial images.

Findings

DeepLabV3+ achieves the best segmentation performance.

The proprietary dataset improves model training accuracy.

Semantic segmentation effectively identifies parked and moving vehicles.

Abstract

This paper addresses the challenge of parking space detection in urban areas, focusing on the city of Granada. Utilizing aerial imagery, we develop and apply semantic segmentation techniques to accurately identify parked cars, moving cars and roads. A significant aspect of our research is the creation of a proprietary dataset specific to Granada, which is instrumental in training our neural network model. We employ Fully Convolutional Networks, Pyramid Networks and Dilated Convolutions, demonstrating their effectiveness in urban semantic segmentation. Our approach involves comparative analysis and optimization of various models, including Dynamic U-Net, PSPNet and DeepLabV3+, tailored for the segmentation of aerial images. The study includes a thorough experimentation phase, using datasets such as UDD5 and UAVid, alongside our custom Granada dataset. We evaluate our models using metrics like Foreground Accuracy, Dice Coefficient and Jaccard Index. Our results indicate that DeepLabV3+ offers the most promising performance. We conclude with future directions, emphasizing the need for a dedicated neural network for parked car detection and the potential for application in other urban environments. This work contributes to the fields of urban planning and traffic management, providing insights into efficient utilization of parking spaces through advanced image processing techniques.