Modern Smart Cities require highly scalable and interconnected technologies to function across several dispersed locations. Edge AI and Deep Learning, two recent developments in computer vision, merge AI vision with IoT, called (AIoT). With these new technologies, it is now possible to process vast amounts of complex visual data quickly and robustly through decentralization and scalable computer vision systems.
Computer vision, the city’s ” eyes, ” is crucial to managing smart cities. The effectiveness of the city is continuously monitored in smart cities using a combination of low-power sensors, cameras, and AI algorithms. Using computer vision and other similar technologies is exceptionally advantageous to governments. Administrators of cities can combine and manage resources thanks to these technologies.
What Exactly Is Computer Vision?
Computer vision is a field of Artificial Intelligence focusing on collecting, processing, and analyzing digital images. Applications for computer vision were formerly restricted to closed platforms. The development of a new set of applications and technologies, however, was hindered by IP convergence and networking technologies.
This sparked the development of innovations and technology related to the Internet of Things (IoT) and smart cities, wherein interconnected devices combine vision-based data to create high-performance big data platforms.
Deep learning and other contemporary technologies for computer vision have the potential to show that computer vision’s capabilities go beyond those of surveillance and law enforcement. Indeed, security concerns were primarily what drove its initial deployment.
Innovative applications provide new ways to contribute to the notion of smart cities, from driverless vehicles to interactive and intelligent structures. These cover a broad spectrum of options for e-health care, intelligent mobility, surveillance, vehicle identification and tracking, smart parking, crowd density, and monitoring, among other things.
Through computer vision, this special issue intends to give the research community and professionals a forum to present solutions and discuss research issues related to creating smart cities. Furthermore, the spread of computer vision in smart cities may open up new research vistas and fields that can interest readers and researchers.
The Top Computer Vision Use Cases In Smart Cites In 2022
According to statistics by international organizations, cities currently hold more than half of the world’s population and will have more than two-thirds by 2050. Different resources, including the environment, traffic, security, and administration, can be managed using smart technologies.
In other words, an AI infrastructure enables the creation of a sustainable smart city for its citizens. The environment, energy, transportation, and security are just a few industries where AI techniques and resources are used.
Some of the most significant computer vision applications for smart cities include the ones listed below:
Public Space Surveillance
Cities are in charge of maintaining a variety of public infrastructure, including water treatment and distribution systems, electrical networks, telephone equipment, street lighting, highways, tunnels, and bridges. However, the inevitable occurrence of numerous infrastructure-related mishaps can burden even the best-resourced teams, leading to a reaction that is neither rapid nor effective.
On the other hand, if a cloud-based system has access to the video available from all local CCTV networks, analyses it, and automatically advises on prompt measures, city officials will be able to make judgments quickly that are effective and efficient.
Bicycle And Smart Traffic Monitoring
More cars are typically associated with higher urban densities, which leads to increased traffic jams, longer travel times, accidents, localized air pollution, and carbon emissions, as well as an overall feeling of tiredness, tension, and worry.
Using new or existing street cameras, an edge-enabled computer vision system may capture a real-time image of traffic conditions and correlate this data with specially trained machine learning algorithms.
Quality Management/ Control
Smart camera applications offer a scalable solution for automated visual inspection and quality control of production lines and assembly lines in smart factories. Deep learning uses real-time item detection to outperform laborious manual examination in this situation.
Machine learning technologies are more durable than traditional machine vision systems and don’t call for expensive specialized cameras or controlled environments. As a result, several locations and factories may apply AI vision techniques.
Smart Parking Monitoring
Authorities are in charge of managing parking spaces, and using computer vision technologies might help them provide better services. A significant share of daily traffic delays and emissions in a crowded metropolis are caused by drivers hunting for open parking spaces.
Using parking lot AI cameras, it is possible to detect cars entering or leaving a parking place, recognize license plates automatically, and keep track of the time spent parked. A single cloud-based database may be used to store all of the data above, saving local officials money on parking enforcement.
Automation Of Harvest
With manual harvesting being the most prevalent and traditional agriculture primarily reliant on mechanical processes, it is expensive and inefficient.
In recent years, agriculture production has seen the emergence of high-end intelligent harvesting equipment, such as harvesting machines and picking robots based on computer vision technology, signaling a new step in the robotic harvesting of crops. Harvesting activities primarily focus on maintaining product quality to maximize market value.
Security And Governance
For a variety of reasons, governments are heavily investing in smart cities. A key factor in developing smart cities is the capacity to enhance public safety and law enforcement. For that reason, local or national governments can employ computer vision for smart city efforts to maintain peace.
Residents of smart cities can live in a secure environment thanks to the use of computer vision. The creation of a citizen database is aided by using image sensors and face recognition software. It makes it easier to locate and detain unauthorized residents and identify those who have been hurt in an accident by knowing their names.
Public Safety And Health
There are times when municipal officials must respond to completely unanticipated and novel situations, as the global pandemic showed. Computer vision systems can assist public services (including police stations, hospitals, water treatment plants, and traffic management control rooms) in adapting to changing regulations, correctly notifying citizens, identifying noncompliance, and taking appropriate corrective action.
For instance, a health protocol violation in a public setting might be identified, examined, and dealt with more speedily and precisely, reducing the risk of runaway dangers to the local community.
Medical Skill Development
Applications for computer vision are used in self-learning platforms to gauge the proficiency of experienced users. For instance, introducing simulation-based surgical teaching methods has benefited surgical education.
Additionally, the idea of action quality evaluation enables the creation of computer programs that automatically assess the performance of surgical students. As a result, people may get valuable feedback that may aid their skill development.
Monitoring Of Road Conditions
Computer vision-based defect identification and condition evaluation for concrete and asphalt civil infrastructure are currently being developed.
Evaluation of the pavement condition provides information that may be utilized to make more reliable and consistent decisions about the upkeep of the pavement network. Pavement distress assessments are often conducted using high-tech data collection vehicles or on-the-ground surveys.
Conclusion
A smart city understands how to organize itself for responsible resource management, high user quality of life, and sustainable urban, economic, social, and environmental growth. The potential for artificial intelligence to enhance people’s quality of life is huge.
At various moments, we’ve seen how technology can assist cities in becoming more innovative, faster, and smarter. Furthermore, the creation of such infrastructure needs a clear political framework.
