Comparison of Computer Vision and Convolutional Neural Networks for Vehicle Parking Control

Abstract

This study compares two artificial intelligence approaches for parking occupancy detection: computer vision and convolutional neural networks (CNN). A dataset of 1,000 parking images was captured and labeled, using OpenCV in Python for computer vision processing and the YOLO V5 model for CNN. Results showed that the YOLO V5 model achieved 88% precision and 82% sensitivity, outperforming the computer vision method, which achieved 80% precision and 79% sensitivity. The research suggests that while CNNs offer superior performance, computer vision is a more economical option in contexts with limited resources. Future research will focus on evaluating the YOLOv7 version to reduce false positives and combining techniques to achieve a balance between accuracy and efficiency under variable conditions.