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https://github.com/docsallover/real-time-object-detection
Real-Time Object Detection with Single Shot MultiBox Detector (SSD)
https://github.com/docsallover/real-time-object-detection
data-science data-science-projects detection machine-learning numpy opencv python single-shot-detection single-shot-multibox-detector tensorflow
Last synced: 1 day ago
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Real-Time Object Detection with Single Shot MultiBox Detector (SSD)
- Host: GitHub
- URL: https://github.com/docsallover/real-time-object-detection
- Owner: docsallover
- License: mit
- Created: 2024-10-09T10:11:18.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-01-01T08:32:06.000Z (30 days ago)
- Last Synced: 2025-01-01T09:23:32.393Z (30 days ago)
- Topics: data-science, data-science-projects, detection, machine-learning, numpy, opencv, python, single-shot-detection, single-shot-multibox-detector, tensorflow
- Language: Python
- Homepage: https://docsallover.com/blog/data-science/real-time-object-detection-with-ssd/
- Size: 84.3 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# Real-Time Object Detection with Single Shot MultiBox Detector (SSD)
This project demonstrates the implementation of the Single Shot MultiBox Detector (SSD), a state-of-the-art object detection algorithm, for real-time object detection. SSD is designed to be fast and efficient, making it suitable for various applications such as surveillance, autonomous vehicles, and augmented reality.
## Overview
The SSD architecture differs from traditional two-stage detectors (like Faster R-CNN) by performing object detection and classification in a single pass. It uses a series of convolutional layers to extract features from an input image and then applies multiple default bounding boxes at different scales to predict object locations and classes.
The system consists of four main components:
1. Speed: SSD is significantly faster than traditional two-stage detectors, making it suitable for real-time applications.
2. Customizable Model: The YOLOv3 model can be trained on custom datasets to detect objects beyond helmets and number plates, adapting it to specific use cases.
3. Accuracy and Precision: YOLOv3 exhibits high accuracy and precision in object detection tasks, ensuring reliable identification of helmets and number plates.
4. Integration with OpenCV: Seamless integration with OpenCV facilitates image preprocessing, visualization, and other computer vision operations.
## How to Use
To use the system, follow these steps:
1. Clone the repository.
2. Create a virtual environment (venv or virtualenv) in the project directory.
3. Activate the virtual environment.
4. Install the required dependencies.
- Run `pip install -r requirements.txt`.
5. Prepare Data:
- Ensure you have a dataset containing images with annotated objects (e.g., COCO, Pascal VOC).
6. Train the Model (Optional):
- If you need to train the SSD model on your custom dataset, follow the provided training instructions.
7. Run the `ssd_object_detection.py` file to execute the system.
- If you are using Python 3, you can run `python ssd_object_detection.py`.
## Dependencies
The system requires the following dependencies:
- OpenCV
- TensorFlow/Keras
- NumPy
- imutils
## License
This project is licensed under the MIT License. See the LICENSE file for more details.
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