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：停车引导系统作为智慧城市发展范式的一部分，近年来已成为一种流行趋势。这类系统的核心在于能够引导驾驶员在感兴趣区域内搜索可用停车位的算法。解决该问题的经典方法是将神经网络分类器应用于摄像头记录数据。然而，现有系统在特定视觉条件下表现出泛化能力不足以及测试不充分的问题。在本研究中，我们全面评估了最先进的停车场占用检测算法，将其预测质量与近期出现的视觉变换器（vision transformers）进行对比，并提出了一种基于EfficientNet架构的新流程。实验结果证明，我们的模型在5个不同数据集上的性能均有所提升。