UWaste

Inspiration

Traditional methods of waste sorting instructions on bins are time-consuming to read, leading to improper waste disposal and environmental concerns. Therefore, we aimed to create an innovative solution to simplify waste sorting and increase awareness among students.

What it does

UWaste serves as a multifaceted solution to waste management and education on university campuses. When in detection mode, the system uses a touchscreen interface and camera to simplify waste sorting for users. Upon presenting an item to the camera, UWaste quickly identifies it using a pre-trained model. The identified waste item is then categorized into waste types, providing users with intuitive guidance on proper disposal. Additionally, when not in detection mode, the touchscreen serves as an advertising platform for educational content and the introduction of the university's waste-sorting game.

How we built it

We developed UWaste using a combination of design and development tools. The user interface design was created using Figma to ensure a visually appealing and intuitive experience for users. The front end of the application was built using HTML, CSS, and JavaScript, making it accessible across various devices. For the backend, we utilized Python along with the TACO database and RoboFlow's pre-trained model for waste detection.

Challenges we ran into

One of the main challenges we faced was integrating the touchscreen interface with the camera and ensuring seamless communication between them. Additionally, fine-tuning the pre-trained model to accurately identify waste items in real-time presented its own set of challenges. Balancing the accuracy of the model with the speed of processing was another hurdle we encountered during development.

Accomplishments that we're proud of

Achieving seamless integration between the touchscreen interface and camera, along with accurate waste detection, showcases our team's dedication and problem-solving skills. Moreover, prioritizing privacy protection by ensuring that no images are stored demonstrates our commitment to user trust and data security.

What we learned

Throughout the development of UWaste, we gained valuable insights into the complexities of waste management and the importance of user-centric design. We learned how to leverage computer vision technology effectively to address real-world challenges and enhance user experiences.

What's next for UWaste

Looking ahead, our goal is to continually improve UWaste by enhancing the accuracy of the waste detection model and expanding its capabilities. We plan to deploy UWaste in strategic locations near rubbish bins where many students pass, maximizing its impact and increasing awareness of waste sorting practices.

Built With

• css
• figma
• html
• javascript
• python
• roboflow
• taco
• yolov8