State-of-the-Art Machine Learning and Deep Learning Techniques for Parking Space Classification: A Systematic Review

Abstract

With the rise of the Internet of Things (IoT), applications have become more competent and smart, and connected devices have given rise to the exploitation of all aspects of a modern city. In today’s era, the problem of parking is also increasing due to the increase in the number of vehicles. Motorists waste time and fuel searching for parking, which may be far from their intended destination. Historically, parking in a congested urban environment has been challenging, frequently depending on manual techniques. Several parking facilities have implemented computerized systems and monitoring technology such as CCTV cameras for tracking car movements. However, these existing systems remain primarily inefficient. This growing challenge emphasizes the pressing demand for enhanced vision and IoT-based solutions to manage parking in urban environments, minimizing time and energy expenditure while improving overall convenience. In the past decade, several research efforts have been conducted to create an intelligent system for detecting and classifying parking spaces, turning into an attractive research domain. To build such a system, researchers have employed various machine learning (ML), deep learning (DL), and IoT. These techniques have been explored to enhance the effectiveness and utility of smart parking. This review paper provides an extensive, comparative, and systematic examination of parking space detection and classification methods. The study provides a detailed discussion of the publicly available datasets used for the performance evaluation of existing ML, DL, and vision techniques integrated with IoT. The review identifies the gaps in existing parking space detection and classification techniques, which further require investigation to improve the effectiveness and capability of smart parking.