Revising deep learning methods in parking lot occupancy detection

TL;DR

This paper evaluates current parking lot occupancy detection algorithms, compares them with vision transformers, and proposes an improved pipeline based on EfficientNet, demonstrating enhanced performance across multiple datasets.

Contribution

It introduces a new EfficientNet-based pipeline for parking occupancy detection and provides a comprehensive evaluation of existing methods and vision transformers.

Findings

The proposed EfficientNet-based model outperforms existing algorithms.

Vision transformers show competitive prediction quality.

The model achieves better generalization across diverse datasets.

Abstract

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.