Hornet Detection and Tracking Project

The Hornet Detection and Tracking project automates the identification and tracking of hornets in video footage to support ecological monitoring and pest control. This solution leverages computer vision to streamline detection.
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Problem Statement

Hornets threaten bee populations and ecosystems. Manual detection methods aren't scalable, necessitating an automated detection system.

Solution

In a team of 3, we developed a local computer vision application on Streamlit using YOLO for object detection to detect and track hornets.
I led the model training and evaluation on this dataset, while other team members focused on preprocessing.

Technologies Used

• Python
• Roboflow for Annotation
• YOLOv7 and v8 for object detection and tracking
• Google Colab for training on GPU

Impact

The system reduces manual monitoring efforts by automating hornet detection and tracking. This solution can scale to other ecological monitoring applications.

Project Showcase

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