kbhujbal/SubmarineShield-underwater_anomaly_recognition_svm
⚓ ML system for submarine threat detection using SVM to classify sonar signals as mines or rocks. Features GridSearchCV optimization, sklearn pipelines, and comprehensive evaluation metrics for underwater anomaly recognition.
This system helps submarine operators and naval defense personnel enhance safety by automatically identifying underwater mines. It takes raw sonar chirp signals, which are measurements of frequency energy across 60 bands, and classifies them as either a rock or a mine. The output is a clear indication of a potential threat, allowing for quick and informed decisions to protect submarine operations.
Use this if you need to reliably distinguish between underwater mines and rocks based on sonar signal data to improve submarine safety and operational efficiency.
Not ideal if you are looking for a system that can detect a wider range of underwater anomalies beyond just mines and rocks, or if you have a very large and diverse sonar dataset that might benefit from more complex deep learning models.
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Language
Python
License
MIT
Category
Last pushed
Nov 24, 2025
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