Parking guidance systems have recently become a popular trend as a part of the smart cities' paradigm of development. The crucial part of such systems is the algorithm allowing drivers to search for available parking lots across regions of interest. The classic approach to this task is based on the application of neural network classifiers to camera records. However, existing systems demonstrate a lack of generalization ability and appropriate testing regarding specific visual conditions. In this study, we extensively evaluate state-of-the-art parking lot occupancy detection algorithms, compare their prediction quality with the recently emerged vision transformers, and propose a new pipeline based on EfficientNet architecture. Performed computational experiments have demonstrated the performance increase in the case of our model, which was evaluated on 5 different datasets.

翻译：停车引导系统近年来已成为智慧城市发展范式中的流行趋势。该类系统的核心在于算法，使得驾驶员能够在感兴趣区域内搜索可用停车位。经典方法基于神经网络分类器应用于摄像头记录。然而，现有系统在泛化能力和针对特定视觉条件的适当测试方面存在不足。在本研究中，我们全面评估了前沿的停车位占用检测算法，将其预测质量与近期出现的视觉Transformer进行比较，并提出了一种基于EfficientNet架构的新流程。实验证明，我们的模型在5个不同数据集上的性能有所提升。