Coastal Cleanup Intelligence Platform: AI-Powered Plastic Litter Classification and Monitoring System for Marine Environmental Protection


Marine pollution, particularly plastic waste, has become a global crisis with severe environmental consequences. Understanding the types and sizes of plastic litter that accumulate on coastlines is crucial for identifying pollution sources, tracking inflow routes, and assessing the overall state of contamination. Traditional methods of data collection and analysis are labor-intensive and time-consuming, necessitating an AI-driven solution for automated and efficient processing.


This groundbreaking AI-based coastal plastic litter classification system streamlines the process of identifying and analyzing plastic waste on coastlines. By utilizing advanced image recognition algorithms, the system categorizes and quantifies plastic litter from user-submitted photos, providing valuable insights into the scope and nature of marine pollution. The platform aims to raise awareness and inspire action to combat this pressing environmental issue.


User Registration and Image Upload: Allow users to create an account, upload photos of coastal plastic litter, and input relevant metadata, such as location, weather conditions, and capture date and time.

AI-Powered Classification: Leverage deep learning and computer vision techniques, such as OpenCV and convolutional neural networks, to automatically classify the type and size of plastic litter in uploaded images.

Result Statistics: Provide comprehensive statistics on classified plastic litter, offering valuable insights into pollution patterns, sources, and trends, ultimately informing targeted cleanup efforts and prevention strategies.


Project Duration: 4 months

Team Composition:

1 Bridge Engineer: Facilitates communication between the client and the development team to ensure project alignment.

1 Technical Leader: Oversees the technical aspects of the project, ensuring code quality and architectural integrity.

2 Developers: Design, code, and test the platform’s features and functionalities, working closely with the bridge engineer and technical leader to deliver a robust and user-friendly solution.


UI design, basic design, development, testing

Tech Stack

React.js, Python, OpenCV, Deep Learning

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