Buoy.
Navigating the complexities of autonomous maritime systems requires precise and reliable object detection capabilities. In our project for Machine Learning 2 at Thomas Jefferson High School for Science and Technology (TJHSST), we developed an advanced buoy detection algorithm designed to enhance the safety and efficiency of autonomous watercraft operations.
Project Overview
Our project focuses on creating a robust machine learning model capable of accurately identifying buoys in various aquatic environments. By leveraging state-of-the-art computer vision techniques, we trained our model to detect buoys under diverse conditions, including varying light, weather, and water surface states.
Key Features
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Dataset Compilation: We curated a comprehensive dataset comprising images of buoys captured in different scenarios to ensure the model's adaptability and resilience.
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Model Architecture: Utilizing convolutional neural networks (CNNs), our algorithm processes visual data to detect and classify buoys with high precision.
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Performance Optimization: Through iterative training and hyperparameter tuning, we achieved a balance between detection accuracy and computational efficiency, making the algorithm suitable for real-time applications.
Applications
The successful implementation of this buoy detection algorithm holds significant potential for various applications, including:
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Autonomous Navigation: Enhancing the navigational capabilities of unmanned surface vehicles (USVs) by providing real-time buoy detection to avoid collisions and adhere to maritime navigation rules.
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Maritime Safety: Assisting in monitoring and maintaining safe waterways by detecting navigational aids and potential hazards.
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Environmental Monitoring: Facilitating the study of marine environments by accurately identifying and tracking buoys used in scientific research.