Smart Waste Management Using CV and ML

Inspiration The growing problem of urban waste mismanagement inspired us to build an intelligent, automated system to detect and classify waste using Computer Vision and Machine Learning, helping cities become cleaner and more sustainable.

What it does Our system uses image processing to detect waste in real time and classify it into categories (e.g., organic, recyclable) using a trained ML model, enabling efficient segregation and management.

How we built it We used Python and OpenCV for image processing, a convolutional neural network (CNN) for classification, and a webcam or static image dataset for testing. We trained the model on labeled waste images and integrated it into a Python-based application.

Challenges we ran into Limited labeled datasets for specific waste types

Achieving high accuracy in real-world scenarios

Processing speed and hardware limitations

Accomplishments that we're proud of Successfully trained and deployed a working waste classification model

Created a basic prototype that can assist smart bins or waste monitoring systems

Developed a low-cost, scalable solution

What we learned The importance of quality datasets and preprocessing

Real-time processing challenges in CV and ML integration

How ML can significantly impact environmental management

What's next for Smart Waste Management Using CV and ML Integrating IoT sensors for smarter decision-making

Developing a mobile app for real-time waste monitoring

Deploying the solution on edge devices or smart bins for field testing

Built With

• cv
• flask
• kaggle
• machine-learning
• python