Background

Our team was tasked with developing a solution to detect intruders using drone images. The goal was to create a computer vision model that could analyze publicly available drone images and identify suspicious individuals.

Problem

The challenge we faced was training the computer vision model to accurately identify potential intruders in the drone images. We needed to ensure that the model could distinguish between normal activity and suspicious behavior, while also minimizing false positives.

Solution

To address this problem, we used machine learning algorithms to train our computer vision model on a large dataset of drone images. We labeled each image with information about whether it contained an intruder or not, and used this data to teach the model how to recognize suspicious behavior.

We also incorporated advanced image processing techniques, such as edge detection and object recognition, to enhance the accuracy of our model. This helped us reduce false positives and improve overall performance.

Impact

Reduced response time by X%
Increased accuracy by X%
Improved security measures for public areas
Potential for future use in law enforcement and military operations

Team

Maria Rodriguez - Project Manager
- Oversaw project planning and execution
- Liaised with stakeholders and clients
Alexander Lee - Machine Learning Engineer
- Built and trained the computer vision model using machine learning algorithms
- Incorporated advanced image processing techniques into the model design
Jasmine Patel - Data Scientist
- Analyzed large datasets of drone images to label them for training purposes
- Cleaned and preprocessed data for use in machine learning algorithms
Tyler Nguyen - Software Developer
- Built software infrastructure for deploying the computer vision model in real-time scenarios
- Leveraged cloud computing platforms for scalability and reliability of software system >
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Drone-based Intruder Detection with ML