End_to_End_Pipeline_Taffic_Safety_Using_Object_Detection

🚦 Traffic Safety Object Detection Pipeline

This project implements an end-to-end pipeline for traffic safety using object detection with YOLOv5, containerization with Docker, and automation using Jenkins and GitHub Actions.

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📌 Project Workflow

1. Dataset Preparation

   • Created custom images and labels using Jheta AI.
   • Exported labeled data in YOLO format.

2. Model Training

   • Used the labeled dataset in a Jupyter Notebook for training.
   • Generated custom weights (best.pt) for object detection.
   • Framework: YOLOv5.

3. Object Detection

   • Implemented detection pipeline using YOLOv5.
   • Configured necessary project files (data.yaml, custom_yolov5m.yaml, etc.).
   • Tested inference on sample images and videos.

4. CI/CD Pipeline

   • Created a Dockerfile to containerize the application.
   • Integrated Jenkins for continuous integration and deployment.
   • Configured GitHub Actions workflow for automated testing and builds.

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⚙️ Tech Stack

• Python 3.10
• PyTorch & YOLOv5
• Jheta AI (for dataset labeling)
• Docker
• Jenkins
• GitHub Actions

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🚀 How to Run

1. Clone this repo:

   git clone https://github.com/TheMatrix31415926/End_to_End_Pipeline_Taffic_Safety_Using_Object_Detection.git
   cd End_to_End_Pipeline_Taffic_Safety_Using_Object_Detection
   

Build Docker image:

docker build -t traffic-safety-objdet .

Run container:

docker run -p 5000:5000 traffic-safety-objdet

Test object detection:

Place your input image in data/

Run detection inside container

Results will be saved in runs/detect/exp*

🎯 Results

Successfully trained a custom YOLOv5 model for traffic safety detection (bikes, helmets, persons, etc.).

Integrated end-to-end automation with Docker + Jenkins + GitHub Actions.