ISD: Real-Time AI-powered Safety Gear Detection System

ISD is an AI-powered computer vision system designed to monitor, track, and enforce workplace safety gear compliance. It utilizes real-time image analysis to identify five essential safety items: helmet, gloves, jacket, goggles, and footwear. ISD verifies if personnel are wearing these items before granting access to the work area. For implementation of Industry saftey Yolo7 model of computer vision is used, which has smaller size.

• Yolov7 Github repo link (https://github.com/WongKinYiu/yolov7)

Flow Diaglram of MLops pipeline

Project Structure Explaintation

• Data Acquisition: Image Annotation and Download: Images are annotated and labeled using Roboflow. The labeled data is downloaded as a ZIP file from an AWS S3 bucket and then unzipped.

• Data Validation Ensuring Data Integrity: This stage verifies that all necessary files generated by the data acquisition component exist. Any missing files are captured in a validation report.

• Model Training (if data validation passes): Leveraging YOLOv7 for Efficiency: Since no image preprocessing is required, the validated images are directly fed into a pre-trained YOLOv7 model for training. YOLOv7 is chosen for its faster inference speed and higher accuracy due to its smaller size and efficient architecture.

• Model Deployment: Cloud Storage and Containerization: The trained model is uploaded to an AWS S3 bucket for storage. The entire pipeline, including the model, is containerized using Docker and deployed to AWS EC2 instances. CI/CD automation via GitHub Actions is implemented to manage the deployment process.

• User Interface: Flask-based Application: A user-friendly application is built using Flask to interact with the deployed pipeline.

Benefits

• Enhances workplace safety by ensuring proper safety gear usage.
• Improves efficiency with real-time monitoring and automated access control.

Workflows

• constants
• config_entity
• artifact_entity
• components
• pipeline
• app.py

Git commands

git add .

git commit -m "Updated"

git push origin main

AWS Configurations

#aws cli download link: https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html

aws configure

How to run?

conda create -n safety python=3.11.9 -y

conda activate safety

pip install -r requirements.txt

python app.py

AWS-CICD-Deployment-with-Github-Actions

1. Login to AWS console.

2. Create IAM user for deployment

#with specific access

1. EC2 access : It is virtual machine

2. ECR: Elastic Container registry to save your docker image in aws


#Description: About the deployment

1. Build docker image of the source code

2. Push your docker image to ECR

3. Launch Your EC2 

4. Pull Your image from ECR in EC2

5. Lauch your docker image in EC2

#Policy:

1. AmazonEC2ContainerRegistryFullAccess

2. AmazonEC2FullAccess

3. Create ECR repo to store/save docker image

- Save the URI: 136566696263.dkr.ecr.us-east-1.amazonaws.com/yolov7app

4. Create EC2 machine (Ubuntu)

5. Open EC2 and Install docker in EC2 Machine:

#optinal

sudo apt-get update -y

sudo apt-get upgrade

#required

curl -fsSL https://get.docker.com -o get-docker.sh

sudo sh get-docker.sh

sudo usermod -aG docker ubuntu

newgrp docker

6. Configure EC2 as self-hosted runner:

setting>actions>runner>new self hosted runner> choose os> then run command one by one

7. Setup github secrets:

AWS_ACCESS_KEY_ID=

AWS_SECRET_ACCESS_KEY=

AWS_REGION = us-east-1

AWS_ECR_LOGIN_URI = demo>>  566373416292.dkr.ecr.ap-south-1.amazonaws.com

ECR_REPOSITORY_NAME = simple-app