Global Perception-Based Robust Parking Space Detection Using a Low-Cost Camera

In the city, parking space detection can help car owners save time in finding parking spaces, and it can also provide help for autonomous parking. However, existing parking space detection methods based on embedded geomagnetic sensors are usually costly and complex to deploy in a large area. To reduce costs while obtaining sufficient performance, it is desirable to detecting parking spaces with cameras. We adopt a low-cost visual method to solve the above problems, using a convolutional neural network to achieve the parking space classification. We propose a general module called Global Perceptual Feature Extractor (GPFE) based on transformer to achieve global attention, which can be easily combined with other classification networks to improve the accuracy and robustness of the entire system, and alleviate the impact of illumination changing and other issues. We verify the proposed method with other methods on three public parking space detection datasets (CNREXT, PKLot and ACPDS). The results show that the accuracy of the network combined with the GPFE module has been generally improved, especially on scenes with low and high illumination, which verifies the robustness and performance of the module.