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Keeping Public Spaces Clean with Technology

Cities use cameras and Digital Twin tech to maintain cleanliness in public areas.

Mateusz Wasala, Krzysztof Blachut, Hubert Szolc, Marcin Kowalczyk, Michal Danilowicz, Tomasz Kryjak

― 6 min read


Tech for Clean Public Tech for Clean Public Spaces and safe. Innovative solutions keep cities clean
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Cleaning public spaces like train stations is important because nobody wants to sit on a bench covered in crumbs or step in a puddle that was there since last week. To help with this, people are using cool tools like Cameras and something called Digital Twin technology. These systems are like having a superhero sidekick for cleanliness!

What’s the Problem?

With more people moving around cities and using public transport, keeping these areas spotless has become a big task. Dirty environments can be both unappealing and unsafe. Imagine slipping on a wet floor or stepping in something squishy-you definitely don’t want to be that person!

So, cities are looking for clever solutions. Enter the tech! Advanced Surveillance systems are stepping up to help maintain cleanliness.

The Magic of Cameras

Video cameras that are set up around public areas do more than just record what’s happening. They can actually spot trash in real time. Imagine a camera that says, “Hey, there’s a wrapper on the ground over by the ticket machine!” This kind of technology helps cleaning staff jump into action right when they’re needed. Talk about a timely response!

Besides just spotting Litter, these cameras can help put together a cleaning schedule that makes sense. If one part of the station is busier than another, the cleaning team can focus their efforts there first. Smart, right?

What is a Digital Twin?

Now, let’s talk about Digital Twin technology. No, it’s not about a twin who got left behind. It’s a digital version of a real-world space, like a train station, that you can use to plan and test things without actually being there.

Think of it as a video game version of reality. In this virtual world, people can simulate different cleaning strategies or see what happens when a new bin model is introduced. It’s like running a “what if” scenario without needing a DeLorean and some flux capacitors.

Real-Life Application: A Train Station

So, how does this all come together? Let’s take a train station as our example. A digital model is made using software that can create 3D environments. The team builds a virtual version of the station, making it look just like the real one but without the mess.

They then set up various things like waste detectors, which can identify where garbage is found. They even simulate how many people are moving around and where the cleaning crew is located. All this happens in the Digital Twin, which allows for fine-tuning before putting everything into practice.

Detecting Litter Like a Pro

The system first picks up trash using advanced camera technology. They use something called YOLO, which stands for “You Only Look Once.” Sounds like a great name for a movie! It’s actually a way for the smart system to recognize different types of litter super fast, even spotting small items that normally would be hard to see.

But here’s the kicker: the cameras have to be set at the right angle and distance to get the best view. It’s like trying to take a selfie without making that awkward face. If they're too far or positioned poorly, the garbage might not be detected.

Checking Bin Occupancy

Another neat trick is figuring out if bins are empty or full. Most solutions involve extra sensors that cost money, but this system tries to do it using just video. Now, how is that possible?

They look at how much of the bin is visible and whether there’s any trash sticking out. If the inside of the bin is all messy but hard to see, it's probably full. Simple yet clever!

Spotting Stains and Other Issues

The team also focuses on detecting stains. This can be anything from water puddles to sticky spots, which can be a bit tricky since they look like many other surfaces. The cameras analyze how much light reflects off these stains compared to the clean areas.

Using special techniques, they can create a clear view of these problem areas. It’s kind of like cleaning your glasses to see clearly, but with cameras instead!

Human Presence and Movement

Not only does the system monitor litter and stains, but it can also see where the cleaning crew is located. Using the same cameras, the program tracks their movements to ensure that every corner of the station is being checked.

This part is like a game of tag, where the cleaning team is “it” and they have to cover the busier areas first. With all this data, the system can advise on the best cleaning routes for staff.

Why Does This Matter?

You may be wondering why all this technology is important. Well, a clean public space increases safety and comfort for everyone. It leads to happier people, and you know what they say: happy people are cleaner people!

Plus, the city saves money by cleaning smarter. Instead of sending a team to the same spot multiple times a day, they can ensure that the cleaning is needed when it’s busy. This means fewer resources wasted and happier cleaning staff too!

Testing the System

To see if it works well, the team tested the technology in a university hallway. Imagine litter scattered all around like confetti at a party. They filmed from different angles to see how well the cameras performed. They even had to retrain their YOLO model to ensure it could spot everything accurately-because nobody wants to miss spotting a rogue coffee cup!

Challenges and Future Plans

Of course, not everything works perfectly 100% of the time. There can be false alarms, like when a passing reflection on a wall gets mistaken for trash. But the system is constantly improving, and with every test, it seems to get a little better!

Looking ahead, the team plans to develop even more features, like spotting when someone is vandalizing public property. They want to ensure every public space is not only clean but protected too.

Conclusion

In summary, using technology like video cameras and Digital Twin models gives cities a better option for maintaining cleanliness. This smart approach ensures that places like train stations are welcoming and safe for everyone. Who would have thought that a little tech could save the day, right?

So next time you step into a sparkling clean train station, you might just be witnessing the magic of modern technology working behind the scenes!

Original Source

Title: Utilisation of Vision Systems and Digital Twin for Maintaining Cleanliness in Public Spaces

Abstract: Nowadays, the increasing demand for maintaining high cleanliness standards in public spaces results in the search for innovative solutions. The deployment of CCTV systems equipped with modern cameras and software enables not only real-time monitoring of the cleanliness status but also automatic detection of impurities and optimisation of cleaning schedules. The Digital Twin technology allows for the creation of a virtual model of the space, facilitating the simulation, training, and testing of cleanliness management strategies before implementation in the real world. In this paper, we present the utilisation of advanced vision surveillance systems and the Digital Twin technology in cleanliness management, using a railway station as an example. The Digital Twin was created based on an actual 3D model in the Nvidia Omniverse Isaac Sim simulator. A litter detector, bin occupancy level detector, stain segmentation, and a human detector (including the cleaning crew) along with their movement analysis were implemented. A preliminary assessment was conducted, and potential modifications for further enhancement and future development of the system were identified.

Authors: Mateusz Wasala, Krzysztof Blachut, Hubert Szolc, Marcin Kowalczyk, Michal Danilowicz, Tomasz Kryjak

Last Update: 2024-11-08 00:00:00

Language: English

Source URL: https://arxiv.org/abs/2411.05964

Source PDF: https://arxiv.org/pdf/2411.05964

Licence: https://creativecommons.org/licenses/by/4.0/

Changes: This summary was created with assistance from AI and may have inaccuracies. For accurate information, please refer to the original source documents linked here.

Thank you to arxiv for use of its open access interoperability.

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