Computer Visionadvanced
Mosquito Detection System - Prevent Mosquito-borne Diseases
The Mosquito Detection System is an end-to-end Computer Vision application designed to detect mosquitoes in images and live camera feeds using the YOLOv5 object detection model. The system aims to support public health initiatives by enabling early identification and monitoring of mosquito presence, helping prevent the spread of mosquito-borne diseases such as Dengue, Malaria, Zika, and Chikungunya.
10 lectures
What You Will Learn
Master the end-to-end Machine Learning project lifecycle from data collection to cloud deployment.
Implement real-time object detection using the YOLOv5 deep learning model.
Understand how to fine-tune pre-trained YOLOv5 models on custom datasets for mosquito detection.
Gain experience building RESTful web applications with Flask for serving ML model predictions.
Develop and deploy Dockerized applications on AWS cloud infrastructure (ECR & EC2).
Build a fully automated CI/CD pipeline using GitHub Actions for continuous integration and deployment.
Learn image preprocessing techniques including Base64 encoding/decoding for web-based inference.
System Architecture
High-level architecture overview of the Mosquito Detection System - Prevent Mosquito-borne Diseases .
What You'll Build
- A "Mosquito Detection System" capable of detecting mosquitoes in images and live camera feeds to help prevent mosquito-borne diseases.
- A YOLOv5-powered detection engine fine-tuned on custom mosquito datasets with optimized inference parameters.
- A Flask-based backend API that accepts image uploads, runs YOLOv5 inference, and returns annotated detection results.
- A user-facing web application with image upload and live webcam detection capabilities using Bootstrap UI.
- A fully automated CI/CD pipeline that builds Docker images, pushes to AWS ECR, and deploys on AWS EC2.
Project Instructor
Boktiar Ahmed Bappy
5+ years exp
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Krish AI
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