How AR, Computer Vision, and AI Are Merging for Smart City Cleanup





Unsurprisingly, urban jungles generate much more waste than cities and towns. As smart cities are at the extreme end of the urbanization spectrum, the waste generated in these places is likely to be enormous. Overall, global waste is expected to increase by around 3.40 billion tonnes by 2050. If not well managed, this accumulated waste can have disastrous consequences for public health and the environment. . Smart cities have the technological means to simplify and make waste management more efficient. Various technologies, such as AR, AI, and computer vision in smart cities, are being used to make these areas clean and sustainable. These technologies help public waste management agencies in smart cities in a variety of ways.

Automated water management

The main reason for improving cleanliness in smart cities is to prevent public health emergencies. Considering this, water management should be one of the highest priorities of smart city governance bodies. Water management issues such as contamination, leaks, and distribution-related issues cause problems in healthcare and other vital sectors such as manufacturing. Authorities in charge of performing urban cleaning can use AI and computer vision in smart cities to continuously monitor water quality and reduce leaks, as it can create multiple puddles full of bacteria in smart cities.

In combination with computer vision and IoT-based purity and turbidity sensors, machine learning can be used to accurately detect contamination levels in water. These tools are also useful for tracking water flow, which is useful for detecting dirty areas in complex pipe networks. Based on data captured by IoT sensors, AI-based tools can determine factors such as total dissolved solids (TDS) levels and PH of water that is being treated for distribution. These tools classify water bodies according to these parameters. Training AI models for such tools involves analyzing thousands of data sets to predict the quality of a given water sample.

As noted above, water leaks can cause hygiene issues in smart cities. Leaks and wasted water hamper household and industrial cleaning. In addition, water shortage and leakage lead to sludge dewatering and agricultural problems. To address these issues, smart cities use computer vision-based smart cameras and sensors near swimming pools, tanks, and reservoirs to trigger leak or loss alerts. AI-based leak detection systems can use sound sensors to detect leaks in pipeline networks. These systems detect leaks by evaluating sounds in water pipes.

As you can see, AI and computer vision in smart cities play an important role in autonomously managing water distribution, monitoring purity levels, and preventing waste.

Classification and recycling of waste

Most smart cities strive to be 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 these industrialization zones can be environmentally sustainable. The detection and classification of waste is vital for the treatment and recycling of waste. Waste that can be identified and classified can be recycled much more efficiently. The use of computer vision in smart cities makes the detection and classification of waste autonomous. Ultimately, the use of computer vision in smart cities can limit their contribution to inevitable climate change.

Recycling is a long and complicated process. The first step to properly recycling waste is to optimize waste treatment. Waste treatment facilities in smart cities separate recyclable waste from the rest based on its ability to be treated and reused. In such processes, achieving 100% accuracy is difficult if only manual or standard automation tools are used. These basic tools cannot visually process and analyze waste to infer recyclability based on composition and other characteristics. Recycling rates can be improved by using AI and computer vision in smart cities to monitor waste and help segregate waste.

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 in different corners of a smart city. IoT sensors, once again, are used at multiple monitoring points in trash cans, allowing artificial intelligence and computer vision tools to determine aspects such as mass balance, purity and composing, among others. Overall, computer vision improves the waste categorization process by reducing the percentage of recyclable waste before actual recycling can take place.

Robotics, another AI-based application, is increasingly appearing in processes involving waste recycling in smart cities around the world. Specialized “recycling robots” are autonomously directed by computer vision-based waste separator discoveries. Robotic arms can pick up and separate waste collected in smart cities into various containers – wet recyclable waste, dry recyclable waste, toxic waste, among others. In addition to making the waste management process self-sufficient, recycling robots are also highly relevant in the current pandemic era, as they allow waste management facility workers to keep their distance from garbage collected from different areas – and the homes of potentially infected patients – in a smart city.

Robots used in waste processing use computer vision, color-coded cameras, laser sensors and metal detectors to classify waste before directing it to the different types of processing areas – recycling, biodegradation and others . Multi-armed robots and suction tools speed up separation. Then, once the recyclable materials have been separated from other waste, robots make the treatment process autonomous. Generally, waste recycling involves steps such as heating and melting the waste. These processes involve several hazardous agents such as high temperatures and pressures, volatile chemicals and others. The use of autonomous robots allows waste recyclers to protect their workers from these agents. The robots can withstand pressure, temperature and abrasive chemicals to effectively facilitate the recycling process. This is also where augmented reality comes in, as waste recycling scientists can use their mobile devices to monitor the recycling process and also remotely guide robots to perform the operations accurately and error-free.

Recycling is an integral part of waste management, circular economy and environmental sustainability. In addition to material separation, robots can also be used for autonomous quality control during waste management.

Robotics, AI, IoT and computer vision in smart cities eliminate human error in waste classification, treatment and recycling, improve waste management and improve the cleanliness aspect of smart cities , while enabling smart cities to achieve nearly the ideal of a true circular economy.

Waste management training and monitoring

Technologies such as augmented reality and virtual reality address one of the main needs of smart city waste management: to make training programs better and more realistic for new workers once the old workforce is gone. work finally retires. AR-based tools allow workers to know their roles with precision. The learning experience of different aspects of waste management is much better when workers can actually perform tasks in an imaginary simulation created by AR or VR powered devices instead of relying on a rulebook, a website or other standard training resource.

Additionally, as noted above, AR is a useful tool for controlling robot cleaners. Thus, users can use their mobile devices to actively monitor the progress of these smart city cleaners. A combination of computer vision, AI, and AR can automate multiple processes, such as individual housing corporations’ garbage collection in smart cities, allowing waste managers to know in real time which locations are cleaned by cleaner robots and those that remain. Based on this information, such robots can be dynamically managed by officials. AR creates different colored areas for this purpose – red for dirty, green for clean – making it easier to tell them apart. This particular feature of AR tools reduces the chances of certain places being cleaned twice or three times, which is useful for optimizing resource usage.

Some surfaces and areas need to be cleaned with more pressure and with more cleaning media. Based on color-coded information from AR apps, manual or robotic cleaners can use the pressure or cleaning equipment needed to clean these 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 table – IoT captures information and powers the tools that will process it, computer vision and AI evaluate information and use pattern recognition and data classification to simplify waste management in smart cities and finally, AR makes cleaning and segregation monitoring much simpler for the designated authorities in charge of smart city cleaning.




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