SmartWaste
AI Powered Garbage and Spill detection with Automate task allocation to Cleanup crew
Created on 25th April 2025
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SmartWaste
AI Powered Garbage and Spill detection with Automate task allocation to Cleanup crew
The problem SmartWaste solves
SmartWaste is an AI-powered tool that makes managing city waste faster, cleaner, and more efficient.
The problem SmartWaste solves are common problems like slow waste detection, manual task assignments, and low community involvement, which lead to delays, poor hygiene, and wasted resources.
With Groq AI and Fluvio, SmartWaste transforms how cities handle waste.
How SmartWaste works:
- AI-powered automated garbage and spill detection: Using Groq AI, SmartWaste analyzes city camera footage to detect garbage and spills in real-time. This ensures quick identification of problem areas, so cleanups can start right away.
- AI-powered chatbot using Groq: A friendly chatbot, powered by Groq AI, lets citizens report waste issues or ask questions easily. It’s fast and simple, making communication smooth for everyone.
- Sentiment analysis using Fluvio and Groq: Fluvio and Groq AI work together to analyze reports from citizens. They figure out which issues are most urgent (like a big spill) by checking the seriousness of each report, so critical cleanups are prioritized.
- Assigns cleaning tasks efficiently: SmartWaste automatically sends the Notifications to cleaning crews to the right locations, reducing delays and improving workflow. This ensures tasks are handled quickly and smoothly.
- Engages automation and public participation: Citizens can report trash on an easy-to-use city map with just a tap. SmartWaste also rewards people for reporting, encouraging everyone to help keep the city clean.
- Seamless coordination: Connects citizens, cleaning crews, and facility managers in real-time for better teamwork.
Why SmartWaste helps you:
Detects and cleans waste faster, keeping your city cleaner.
Saves time by automating tasks and prioritizing urgent issues.
Gets the community involved with an easy reporting system and rewards.
Improves communication between everyone involved.
Challenges we ran into
- Accuracy in Garbage and Spill Detection
One major challenge we faced was achieving high accuracy in detecting garbage and spills from live CCTV footage. Initially, the model struggled with distinguishing between similar background elements and actual waste.
How we solved it: We increased the size of the training dataset and fine-tuned the model with more diverse and labeled samples. We also adjusted training parameters to improve detection accuracy under different lighting and environmental conditions. - Automating Task Allocation
Automatically assigning tasks to the correct cleanup teams in real-time was another challenge. Standard logic-based assignment methods weren't flexible enough for unpredictable urban scenarios.
How we solved it: We implemented a Reinforcement Learning (RL) model with more parameters and state representations. This helped the system learn from different task scenarios and optimize crew assignments dynamically.
Tracks Applied (2)
Groq track
Groq
InfinyOn/Fluvio Track
InfinyOn
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