Car Parking Occupancy Detection Using Deep Learning

Abstract

It can be difficult to find a parking space in a busy neighborhood because you never know when one will become available. Due to the rise of automobiles and fall in parking spots, this task even more challenging. To increase the dependability of smart parking systems, numerous researchers have sought to improve them. In smart parking systems, computer vision is a more advantageous method for detecting parking spaces than sensors. Many parking spots may be monitored by a single smart camera, which is also reasonably easy to install and maintain. To identify parking occupancy in camera-captured photos, a CNN model is employed. The CNN model will be trained using the CNRpark+Ext and PKLot datasets, which contain 4081 and 12,416 parking lot photos under various weather scenarios, respectively. The CNN architectures, CmAlexNet and mAlexNet, are five-layer designs built on the eight-layer AlexNet architecture, in which processing speed is increased by removing the completely connected layer and the third and fourth convolutional layers. To increase accuracy, the first convolutional layer of CmAlexNet's filter is resized from 11x11 to 13x13, and batch normalization [1] is used instead of the original method. Three key performance metrics are evaluated between CmAlexNet and mAlexNet: recall, precision, and accuracy in classification. Keywords: Car Parking , Computer Vision , Deep learning , CNN, mAlexnet , cmAlexnet , CNRpark dataset , PKLot dataset.