https://github.com/Daheer/Driving-Environment-Detector

Detecting road objects using YOLO CNN Architecture
https://github.com/Daheer/Driving-Environment-Detector

computer-vision deep-learning detection tensorflow yolov2

Last synced: 12 days ago
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Detecting road objects using YOLO CNN Architecture

• Host: GitHub
• URL: https://github.com/Daheer/Driving-Environment-Detector
• Owner: Daheer
• License: mit
• Created: 2022-06-14T16:34:35.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2023-11-20T08:19:27.000Z (7 months ago)
• Last Synced: 2024-02-29T10:31:14.789Z (4 months ago)
• Topics: computer-vision, deep-learning, detection, tensorflow, yolov2
• Language: Python
• Homepage:
• Size: 6.79 MB
• Stars: 0
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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• awesome-yolo-object-detection - Daheer/Driving-Environment-Detector - Environment-Detector?style=social"/> : Detecting road objects using YOLO CNN Architecture. (Applications)

README

        
# Driving-Environment-Detector

**Driving-Environment-Detector** recognizes everyday road objects on a road scene. It is based on the You Only Look Once CNN Architecture, specifically the YOLO v2 Darknet 19. 

![Yolo v2 Darknet 19](images/yolo_v2_darknet19.png "Yolo v2 Darknet 19")

# Model Architecture Plot

[Click to view full architecture](images/yolo_model_architecture.png)

![Yolo Driving Environment Model Architecture](images/yolo_model_architecture_short.png "Yolo Driving Environment Model Architecture")

# Built Using

- [Python](https://www.python.org)

- [Tensorflow](https://www.tensorflow.org)

- [OpenCV](https://opencv.org/)

- Others

# Prerequisites and Installation



    
 
  Python  

        

    python driving_environment_detector.py

        





# Project Structure

```

.

├── README.md

├── FiraMono-Medium.otf

├── SIL Open Font License.txt

├── Images

│   ├── sample.png

│   ├── yolo_model_architecture_short.png

│   ├── yolo_model_architecture.png

│   ├── yolo v2 darknet19.png

│   ├── sample_input.png

│   └── sample_input.png

├── model data

│   ├── variables

│   │   ├── anchors.txt

│   │   ├── coco_classes.txt

│   │   ├── pascal_classes.txt

│   │   ├── saved_model.pb

│   │   └── yolo_anchors.txt

│   └── yad2k

│   │   ├── __pycache__

│   │   ├── models

│   │   └── utils

│   │   │   └── util.py

├── .gitattributes

├── driving_environment_detector_voila.ipynb

├── driving_environment_detector.ipynb

├── driving_environment_detector.py

└── requirements.txt

```

# Usage

> Simply place your video covering a road scene in the top directory. Run the installation code, sip

some coffee or take a walk depending on the legth of your video :). When completed, the new video 

can be found in out/output_video.mp4

# Demo

Sample Input               |  Sample Output

:-------------------------:|:-------------------------:

![](images/sample_input.png) |  ![](images/sample_output.png)

# References

- [SlimDeblurGAN-Based Motion Deblurring and Marker Detection for Autonomous Drone Landing - Scientific Figure on ResearchGate.](https://www.researchgate.net/figure/YOLOv2-backbone-convolutional-neural-networks-CNN-architecture-The-backbone-network-is_fig3_342941568)

- [Convolutional Neural Netwokrs](https://www.coursera.org/learn/convolutional-neural-networks/home/)

# Contact

Dahir Ibrahim (Deedax Inc) - http://instagram.com/deedax_inc 


Email - [email protected] 


YouTube - https://www.youtube.com/channel/UCqvDiAJr2gRREn2tVtXFhvQ 


Project Link - https://github.com/Daheer/Driving-Environment-Detector 


Twitter - https://twitter.com/DeedaxInc