Data Augmentation for Image based Parking Space Classification Deep Model

Hojin Yoo, Kyungkoo Jun

http://doi.org/10.5626/JOK.2022.49.2.126

A parking occupancy state determination system using an ultrasonic sensor or a camera is mainly used in indoor parking lots. However, in the case of an outdoor parking lot, there is a limit to the introduction of these systems due to the high installation cost and accuracy problems. In addition, the application of deep learning is restricted because it is difficult to obtain representative learning data due to diverse lighting conditions, camera positions, and features. In this paper, we analyzed the effect of augmentation techniques on the performance of a deep model for parking status classification in such a data shortage situation. To this end, the parking area images were classified by situations. Four augmentation techniques were applied to the training of ResNet, EfficientNet, and MobileNet. Based on performance evaluation, the accuracy was improved by up to 5.2%, 8.67%, and 15.44%p in the case of mixup, stopper, and rescaling methods, respectively. On the other hand, in the case of center crop, which was known to have performance improvement in other studies, the accuracy decreased by an average of 4.86%p.