Revising deep learning methods in parking lot occupancy detection
This work addresses the need for improved generalization in parking guidance systems for smart cities, but it is incremental as it builds on existing methods.
The study tackled the problem of parking lot occupancy detection by evaluating existing deep learning methods and proposing a new pipeline based on EfficientNet, resulting in a performance increase demonstrated across 5 datasets.
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.