How AR, Computer Vision And AI Coalesce For Smart City Cleaning – Forbes

Computer vision in smart cities

Rather unsurprisingly, urban jungles generate much more waste than towns and villages. As smart cities are on the extreme end of the urbanization spectrum, the waste generated in such places is expectedly huge. Generally speaking, global waste is expected to increase by about 3.40 billion tonnes by 2050. If not managed well, this accumulated waste can have disastrous implications for public health and the environment. Smart cities have the technological means with which waste management can be simplified and made more effective. Various technologies, such as AR, AI and computer vision in smart cities, are used to make such zones clean and sustainable. These technologies assist public waste management agencies in smart cities in a variety of ways.

The main reason for improving cleanliness in smart cities is to prevent public health emergencies. Considering that, water management should be one of the biggest priorities for smart city governance bodies. Water management issues such as contamination, leakages and distribution-related problems cause problems in healthcare and other vital sectors such as manufacturing. Authorities tasked with carrying out urban cleaning can use AI and computer vision in smart cities to constantly monitor water quality and reduce leakages as they can create several bacteria-ridden puddles in smart cities.

In combination with computer vision and IoT-based purity and turbidity sensors, machine learning can be employed to accurately detect contamination levels in the water. Such tools also come in handy to trace water flow, which is useful for detecting the filthy areas in complex pipeline networks. Based on the data captured by IoT sensors, AI-based tools can determine factors such as the Total Dissolved Solids (TDS) levels and PH of water that is being processed for distribution. Such tools categorize water bodies based on such parameters. The training of AI models for such tools involves the analysis of thousands of datasets to predict the quality of a given water sample.

As stated above, water leakages can cause hygiene-related problems in smart cities. Water leakage and wastage are detrimental to domestic and industrial cleaning purposes. Additionally, water shortage and leakage result in problems in sludge dewatering and agriculture. To address such problems, smart cities use computer vision-based intelligent cameras and sensors near pools, tanks, reservoirs to raise leakage or loss alerts. AI-based leakage detection systems can use sound sensors to detect leaks in pipeline networks. Such systems detect leaks by assessing the sounds in water pipes.

As you can see, AI and computer vision in smart cities play significant roles in autonomously managing water distribution, monitoring purity levels and preventing wastage.

Most smart cities strive to be a part of an ideal circular economy where every product is 100% recyclable. A circular economy, although difficult to achieve, is one of the ways in which such zones of industrialization can be environmentally sustainable. Garbage detection and classification are vital for the processing and recycling of waste. Garbage that can be identified and classified can be recycled much more effectively. The use of computer vision in smart cities makes garbage detection and classification autonomous. In the long run, the use of computer vision in smart cities makes it possible to limit their contribution to inevitable climate change.

Recycling is a long and complicated process. The first step of getting garbage recycling right involves optimizing waste processing. Waste treatment facilities in smart cities segregate recyclable waste from the rest on the basis of their capacity to be processed and reused. In such processes, achieving 100% accuracy is challenging if only manual labor or standard automation tools are used. Such basic tools cannot carry out visual processing and analysis of waste materials to deduce recyclability on the basis of composition and other characteristics. Recycling rates can be improved by using AI and computer vision in smart cities for monitoring waste and assisting with waste segregation.

Computer vision-based tools can improve decision-making in such processes and eliminate any anomalies. AI and computer vision use algorithms and deep learning for the analysis of daily waste generated from various corners of a smart city. IoT sensors, once again, are used in multiple monitoring points in trash cans which enables AI and computer vision tools with determining aspects such as mass balance, purity and composition, among others. All in all, computer vision improves the process of garbage categorization by reducing the percentage of wasted recyclables before the actual recycling can take place.

Robotics, another AI-based application, has increasingly emerged in processes involving waste recycling in smart cities around the world. Specialized "recycling robots" are autonomously directed by the findings of computer vision-based garbage segregators. Robotized arms can pick and separate waste collected in smart cities in various containerswet recyclable waste, dry recyclable waste, toxic waste, among others. Apart from making the process of waste management autonomous, recycling robots are also highly relevant in the current pandemic age as they allow workers in waste management facilities to keep their distance from the garbage collected from different zonesand potentially infected patients homesin a smart city.

Robots used in waste processing use computer vision, color-coded cameras, laser sensors and metal detectors to classify the waste materials before directing them towards the different kinds of processing zonesrecycling, biodegradation and others. Robots with several arms and suction tools make segregation faster. Then, once the recyclables are separated from the other waste materials, robots make the process of processing autonomous. Generally, waste recycling involves steps such as heating and melting waste materials. Such processes involve several dangerous agents such as high temperature and pressure, volatile chemicals and others. Using autonomous robots allows waste recyclers to protect their workers from such agents. Robots can withstand the pressure, temperature and abrasive chemicals to facilitate the recycling process efficiently. This is also where AR enters the fray as the scientists tasked with recycling waste can use their mobile devices to monitor the recycling process and also remotely guide robots to carry out the operations precisely and without errors.

Recycling is a massive part of waste management, circular economy and environmental sustainability. Apart from segregating materials, robots can also be used for autonomous quality control during waste management.

Robotics, AI, IoT and computer vision in smart cities eliminate human error from waste classification, processing and recycling, making waste management better and enhancing the cleanliness aspect of smart cities as well as enabling smart cities to almost realize the ideal of a truly circular economy.

How AR, Computer Vision And AI Coalesce For Smart City Cleaning

Technologies such as AR and VR serve one of the main needs of smart city waste managementmaking training programs better and more realistic for newer workers once the old workforce eventually retires. AR-based tools allow workers to know their roles accurately. The experience of learning the different aspects of waste management is much improved when workers actually get to perform tasks in a make-believe simulation created by AR or VR-powered devices instead of relying on a rulebook, website or another standard training resource.

Additionally, as stated above, AR is a useful tool to control robotic cleaners. So, users can use their mobile devices to actively monitor the progress of such cleaners in smart cities. A combination of computer vision, AI and AR can automate several processes, such as garbage collection from individual housing societies in smart cities, enabling waste management officers to know in real-time the locations that are cleaned by cleaner robots and which ones are remaining. Based on this information, such robots can be managed dynamically by officials. AR creates different color zones for this purposered for dirty, green for cleanwhich makes it easier to differentiate between them. This particular feature of AR tools reduces the chances of certain places being cleaned twice or thrice, which is useful to optimize resource usage.

Certain surfaces and areas need to be cleaned with greater pressure and with more cleaning resources. Based on the color-coded information from AR applications, manual or robotic cleaners can use the necessary pressure or cleaning material to cleanse such areas and surfaces.

The combination of AR, AI and computer vision in smart cities has several other applications. Each technology brings something unique to the tableIoT captures information and actuates the tools that will process it, computer vision and AI evaluate the information and use pattern recognition and data classification to simplify waste management in smart cities and finally, AR makes cleaning and segregation monitoring much simple for the designated authorities tasked with smart city cleaning.

Originally posted here:

How AR, Computer Vision And AI Coalesce For Smart City Cleaning - Forbes