https://github.com/bpavan16/yolo-work
This repository hosts multiple projects leveraging state-of-the-art YOLO models including dental anatomy detection, people counting, and lane change detection.
https://github.com/bpavan16/yolo-work
object-classification object-counting object-detection yolo
Last synced: about 2 months ago
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This repository hosts multiple projects leveraging state-of-the-art YOLO models including dental anatomy detection, people counting, and lane change detection.
- Host: GitHub
- URL: https://github.com/bpavan16/yolo-work
- Owner: bPavan16
- License: mit
- Created: 2024-11-12T21:08:11.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-12-20T18:29:50.000Z (5 months ago)
- Last Synced: 2025-02-15T16:49:33.614Z (3 months ago)
- Topics: object-classification, object-counting, object-detection, yolo
- Language: Jupyter Notebook
- Homepage:
- Size: 40.3 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# Unified Object Detection Projects
This repository hosts multiple projects leveraging state-of-the-art YOLO (You Only Look Once) models for real-time object detection across various domains. Each project highlights specific use cases, including dental anatomy detection, people counting, and lane change detection.
## Projects Overview
### 1. Dental Anatomy Detection System
This project uses YOLOv11 to detect and classify different types of teeth in dental images, labeling them with their anatomical names.
#### Features
- Detects and classifies molars, premolars, canines, and incisors.
- Provides high-accuracy detection and labeling.
#### Prerequisites
- Python 3.x
- OpenCV
- NumPy
- Pillow
- YOLOv11 pre-trained weights
#### Usage
1. Prepare dental images.
2. Download YOLOv11 weights and configuration.
3. Run the detection script to output labeled images.
---
### 2. People Counter
This project counts the number of people in a given video feed or image using YOLO.
#### Features
- Real-time people counting.
- High accuracy with YOLO.
#### Requirements
- Python 3.x
- OpenCV
- NumPy
- YOLO weights and configuration files.
#### Usage
1. Clone the repository:
```bash
git clone https://github.com/yourusername/People-Counter.git
```
2. Install dependencies:
```bash
pip install -r requirements.txt
```
3. Download YOLO weights and run the script:
```bash
python people_counter.py --input path_to_video_or_image --output path_to_output
```
---
### 3. Lane Change Detection
This project uses YOLOv8 to detect lane changes in real-time from video feeds.
#### Features
- Enhanced lane change detection accuracy using YOLOv8.
- Real-time object detection.
#### Requirements
- Python 3.x
- OpenCV
- NumPy
- YOLOv8 pre-trained weights
- Ultralytics library
#### Usage
1. Install YOLOv8:
```bash
pip install ultralytics
```
2. Load YOLOv8 and process the video:
```python
from ultralytics import YOLO
model = YOLO('yolov8.pt')
results = model.detect('video.mp4')
```
3. Run the Jupyter notebook to detect and display lane changes.
---
## Installation
For all projects:
1. Clone the repository.
2. Install the required Python libraries:
```bash
pip install -r requirements.txt
```
3. Download the appropriate YOLO weights and configuration files.
## License
This repository is open-source and available under the MIT License.
## Acknowledgments
- OpenCV for image processing.
- YOLOv8 and YOLOv11 for state-of-the-art object detection.
- NumPy for numerical operations.
- Pillow for image annotation.
- Ultralytics for YOLOv8 support.