Anyone attentive to technological development knows that in recent months there has been no end of talk about artificial intelligence, and it is very clear that when applied to any economic sector or company it allows for greater speed and efficiency, always with due analysis by the professionals in each field.
When talking about AI applications in the detection of free parking spots in cities, it results in improved traffic flow and parking performance. At Urbiotica, we want to take a holistic view of the issue, addressing how technology and AI enable better curbiside management and single space detection.
How artificial intelligence revolutionizes parking spot detection
The use of artificial intelligence (AI) in parking spot detection has completely changed curb management. This technology, along with its branches machine learning (ML) and deep learning (DL), has transformed the way systems detect and manage available spots. From the use of cameras that send data to platforms for processing to the implementation of sensors that learn from the environment, AI has facilitated the creation of highly efficient and adaptive systems in parking environments.
The impact of AI on parking detection lies in its ability to develop algorithms that adapt and improve over time. Deep learning is a technique that mimics the workings of the human brain to help computers learn to identify complex patterns directly from data and without explicit programming. The ability to run this from connected platforms allows for instant adjustments and more advanced processing, so they are able to continuously improve their detection capacity and adapt to new situations.
Machine learning, on the other hand, encompasses both deep learning and other techniques, allowing computers to learn from experience. For example, it is able to identify the content of an image without telling you exactly what it is. Direct application of these algorithms in devices such as surveillance cameras has demonstrated more efficient management, minimizing detection time and optimizing the use of available parking spots.
AI applications using Magnetic Sensors
Another efficient way to use AI for parking detection is through magnetic sensors. These strategically installed sensors detect changes in the magnetic field to determine whether parking spots are occupied. During their initial installation, these sensors learn from their environment and can perform a self-calibration process, in which they adapt to it through machine learning.
Artificial intelligence can reside in these magnetic sensors directly or be set up in a centralized platform. Implementing AI in the cloud offers significant advantages by minimizing maintenance and individual interventions on each sensor. This results in global calibrations that ensure greater energy efficiency and reduce the complexity of maintenance operations.
However, when the intelligence is in the sensor, it can have implications in terms of adaptability, energy consumption, and localized maintenance needs. These aspects must be considered when implementing sensing systems.
AI Camera Applications
One of the most common ways of using artificial intelligence in parking spot detection is through cameras. These cameras can be designed to run AI processes directly on their hardware or be connected to a centralized platform that performs AI operations.
When AI is implemented from a platform, deep learning can leverage multiple layers and a large neural network to perform complex tasks. This offers the advantage of real-time calibration adjustments at scale, facilitating management and operational efficiency.
However, running AI directly on cameras can present limitations in space and processing capacity. This may restrict the amount of information that can be extracted and make it difficult to make localized detailed settings on each device.
Intelligent detection with cameras and AI at Urbiotica
As one of the artificial intelligence-based technologies to detect available parking in cities, we want to talk about U-Spot Visio. This is detection software that processes images taken in real time by compatible cameras.
It allows a large number of parking spots to be monitored with a single camera, thus optimizing implementation costs. This feature makes it ideal for parking lots with a large number of parking spots, such as those at shopping centers, airports, or hospitals.
How does U-Spot Visio and its cameras work to improve the management of free parking spot detection? The most important points are:
1. The cameras send images of the spots to the Cloud.
First, using a standard Internet connection, each camera regularly sends images to the smart parking platform for processing and interpretation. Thus, with a single space detection reliability of more than 99% over the lifetime of the project, there is no need to install extra cameras or specific hardware. It works with the usual cameras on the market and does so in compliance with the GDPR (General Data Protection Regulation).
2. The SMART platform processes the images.
The smart parking platform then employs cloud-based image processing using advanced deep learning algorithms. Thanks to this proprietary AI, it detects the occupancy of each spot in real time with 99% reliability. It is also able to identify changes of vehicles in parking spots, a crucial aspect for camera-regulated parking control. This translates into determining the entry, exit, and time each vehicle spends in the spots.
U-Spot VISIO: A.I. Single-spot detection
Combining detection systems
Depending on the particularities of the project, such as height or visibility, both U-Spot Visio and U-Spot single space wireless sensors are recommended. This combination makes it possible to offer a comprehensive solution for parking guidance and enforcement, adapting to the different needs and characteristics of the surroundings.
The combined use of these advanced technologies allows us to tailor the enforcement and guidance solutions we have developed to the specific needs of any parking project. This combination makes it easier to get accurate real-time parking information, even in areas where the visibility is obstructed by items such as awnings or trees, providing a more effective solution for parking managers in environments such as hospitals, shopping centers, or other venues.
Furthermore, the implementation of AI to promote sustainable mobility, save fuel, and reduce pollution is also crucial to reduce waiting times and congestion from vehicles in search of available parking spots, ultimately improving the drivers’ experience.
Everything we have explained in this article means that there is ample incentive to invest in artificial intelligence in parking detection for reasons of space efficiency, the environment, and economics, because these solutions, like those we develop at Urbiotica, pay for themselves and avoid extra costs with AI and our full range of products and services.
