  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.
