Shot Sense is a gunshot detection app that uses Python and TensorFlow to classify and detect gunshots in real-time. This application aims to enhance safety by providing users with a reliable and responsive gunshot detection system.
- Gunshot Detection: Uses machine learning models built with TensorFlow to accurately detect gunshots.
- Real-Time Notifications: Informs the user if a gunshot is detected or not, with a clear message on the app interface.
- Simple User Interface: The app displays messages like "Gunshot not Detected" to communicate detection results.
- Python: The core language used for building the application.
- TensorFlow: A machine learning framework used to train and implement the gunshot detection model.
- Audio Capture: The app listens for ambient sounds through the device's microphone.
- Classification: The captured audio is processed and passed through a TFlite model to classify whether a gunshot has occurred.
- Notification: Based on the model's classification, the app displays messages like "Gunshot not Detected" or "Gunshot Detected" to inform the user.
The app interface is minimal and intuitive. It provides users with simple messages to indicate detection status. For example, if no gunshot is detected, the screen will show a message like:
"Gunshot not Detected"
- This app can be used in environments where security is a concern, such as schools, public areas, or private properties.
- The app can be integrated with existing security systems to provide an added layer of safety.
- Improved Accuracy: Train the model with a larger dataset to increase detection accuracy.
- Integration: Connect with other security systems, such as CCTV or alarm systems, to provide automated responses.
This project is licensed under the MIT License.