This project aims to develop an object detection model that can accurately detect, classify, and localize multiple objects within an image or video frame. The model is designed for high precision, minimizing false positives and false negatives, and is capable of processing images or video in real-time.
- High Precision Detection: The model is optimized to detect objects with high accuracy, reducing false positives and negatives.
- Real-Time Processing: Capable of processing images and video streams in real-time or near real-time, making it suitable for practical applications.
- Diverse Environment Handling: The solution is robust to various environmental conditions, including different lighting, angles, and object occlusions.
- Real-World Applications: Demonstrated applicability in scenarios such as:
- Surveillance
- Autonomous Driving
- Retail Automation
- Object Tracking: The model includes tracking capabilities to follow detected objects across multiple video frames.
The model has been tested on various datasets and real-world scenarios. Below are some example results:
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Image Detection:
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Video Detection:
These examples demonstrate the model's ability to accurately identify and track objects in different environments.
- YOLOv5 - The object detection architecture used in this project.
This project is licensed under the MIT License - see the LICENSE file for details.