Empty Space Detection with YOLOv8 is a computer vision project that aims to detect falls using the YOLOv8 object detection model. This project provides a real-time Empty Space detection solution by analyzing video streams.
- Utilizes the YOLOv8 object detection model for accurate fall detection
- Real-time detection and immediate alert using visual cues
- With accurate identification of vacant areas, businesses can enhance their restocking processes, improve customer experience, and maximize shelf utilization.
- Built with efficiency and ease-of-use in mind
- Python 3.x
- OpenCV
- Ultralytics YOLOv8
- Clone the repository:
https://github.com/alijawad07/empty_space_shelf_yolov8
- Install the required dependencies:
pip install -r requirements.txt
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Update the configuration file with the appropriate paths and parameters.
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Run the empty_shelf script:
python3 empty_shelf.py --data --source --output --weights
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--data => .yaml file with dataset and class details
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--source => Path to directory containing video
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--output => Path to save the detection results
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--weights => Path to yolov8 weights file
- Thanks to Roboflow for providing the comprehensive fall detection dataset used in training the YOLOv8 model.
- Special appreciation to Ultralytics for developing the YOLOv8 model and its integration with the project.