SAR SHIP DETECTION USING YOLOv8

This project utilizes YOLOv8 for ship detection in Synthetic Aperture Radar (SAR) images and videos. The model detects ships from both images and video feeds uploaded by the user.

🚀 Features

• Image Input: Detects ships in uploaded images.
• Video Input: Detects ships in uploaded video files frame-by-frame.
• Real-time Results: Displays the number of detected ships in the current frame.

📽️ Demo & Visuals

🛠️ Requirements

• Python 3.8+
• Streamlit
• ultralytics (YOLOv8)
• OpenCV
• NumPy
• PIL

🚀 Installation

1. Clone the repository:

   git clone https://github.com/yourusername/sar-ship-detection.git

2. Navigate to the project folder:

   cd sar-ship-detection

3. Install the required dependencies:

   pip install -r requirements.txt

4. Run the app:

   streamlit run app.py

📝 Usage

1. Run the app using Streamlit:

   streamlit run app.py

2. Select input type: Choose between image or video upload.

3. Upload your image/video and see real-time ship detection results.

⚙️ Model Details

The model used in this project is YOLOv8, trained to detect ships in SAR images.

Steps:

• The model uses YOLOv8 architecture.
• Inference is done frame-by-frame for videos, and object detection results are plotted on the image/video.
• Ships detected in the image/video are highlighted, and the total number of ships is displayed.

🔧 Customization

You can retrain the model using your own dataset by modifying the training script (located in the weights/ folder). Ensure that the dataset is preprocessed correctly to achieve optimal results.

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