Convolutional Neural Network for Parking Slots Detection

Abstract

With the rapid growth of transport number on our streets, the need for finding a vacant parking spot today could most of the time be problematic, but even more in the coming future. Smart parking solutions have proved their usefulness for the localization of unoccupied parking spots. Nowadays, surveillance cameras can provide more advanced solutions for smart cities by finding vacant parking spots and providing cars safety in the public parking area. Based on the analysis, Google Cloud Vision technology has been selected to develop a cyber-physical system for smart parking based on computer vision technology. Moreover, a new model based on the fine-tuned convolutional neural network has been developed to detect empty and occupied slots in the parking lot images collected from the KhNUParking dataset. Based on the achieved results, the performance of parking lots’ detections can be simplified, and its accuracy improved. The Google Cloud Vision technology as parking slots detector and a pre-trained convolutional neural network as a feature extractor and a classifier were selected to develop a cyber-physical system for smart parking. As a result of the computational investigation, the proposed fine-tuned CNN managed to process 66 parking slots in roughly 0.14 seconds on a single GPU with an accuracy of 85.4%, demonstrating decent performance and practical value. Overall, all considered approaches contain strengths and weaknesses and might be applied to the task of parking slots detection depending on the number of images, CCTV angle, and weather conditions.