格拉纳达市的停车位检测
Parking Space Detection in the City of Granada
AI总结：
本文针对格拉纳达市城市停车位检测问题，构建专属数据集，对比优化Dynamic U-Net、PSPNet、DeepLabV3+等语义分割模型，实验证明DeepLabV3+性能最优，为城市规划与交通管理提供技术支撑。
摘要：
本文聚焦格拉纳达市，旨在解决城市区域的停车位检测难题。我们利用航空影像，开发并应用语义分割技术，以准确识别停放车辆、行驶车辆与道路。本研究的一项重要内容是构建了格拉纳达专属的专有数据集，该数据集对训练我们的神经网络模型至关重要。我们采用了全卷积网络（Fully Convolutional Networks）、金字塔网络（Pyramid Networks）和空洞卷积（Dilated Convolutions），验证了它们在城市场景语义分割中的有效性。我们的方法涉及对多种模型的对比分析与优化，包括专为航空影像分割定制的Dynamic U-Net、PSPNet和DeepLabV3+。研究包含全面的实验阶段，除了我们自制的格拉纳达数据集外，还使用了UDD5和UAVid等数据集。我们采用前景准确率（Foreground Accuracy）、戴斯系数（Dice Coefficient）和杰卡德指数（Jaccard Index）等指标评估模型性能。结果表明，DeepLabV3+的表现最为出色。我们最后提出了未来研究方向，强调需要专门针对停放车辆检测的神经网络，以及该方法在其他城市环境中的应用潜力。本研究为城市规划与交通管理领域做出贡献，通过先进的图像处理技术为停车位的高效利用提供了见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
