IndustryXpert is a web application designed to revolutionize industrial safety protocols by leveraging advanced technologies. It empowers organizations to create a safer and more secure work environment for personnel and equipment.
Demo Video and blog
You can see a demo of IndustryXpert in action here: IndustryXpert Demo
You can give a read to our blog here: IndustryXpert blog
Key Features:
- Motion Amplification: Identifies subtle equipment movements often invisible to the naked eye, enabling proactive maintenance and preventing unexpected breakdowns.
- Emergency Alert System: Allows individuals to signal for help by making a specific gesture (L pose) in front of a camera, ensuring prompt assistance in emergency situations.
- Restricted Zone Enforcement: Utilizes live CCTV feeds and object detection to identify unauthorized access to restricted areas, facilitating timely intervention and enforcing safety protocols.
- Fire and Safety Gear Detection: Employs machine learning algorithms to identify safety gear worn by personnel and detect potential fire risks in real-time, upholding the highest standards of safety and compliance.
Benefits:
- Early detection of potential issues minimizes downtime and prevents costly emergency repairs.
- Enhanced maintenance allows for targeted repairs and optimization of equipment performance.
- Reduced risk of accidents and injuries by ensuring adherence to safety protocols.
- Improved response times to emergencies through the use of the alert system.
_pycache_: This folder is automatically generated by Python and stores compiled bytecode for faster program execution. You don't need to modify its contents.app.py: This is the core Python script containing the application logic. It defines routes, handles user interactions, and interacts with other components.instance: This folder is typically used for storing runtime data or configuration specific to a particular instance of the application.models: This folder contains yolov8 models for detection.requirements.txt: This text file lists the external Python libraries required for the application to run. Dependencies like Flask, SQLAlchemy, OpenCV, and YOLOv8 are likely specified here.static: This folder stores static assets used by the application, such as images, CSS stylesheets, and JavaScript code. These files are typically served directly by the web server without involving server-side processing.templates: This folder contains HTML templates used to dynamically generate web pages. The application likely uses a templating engine like Jinja2 to render these templates with data retrieved from the database or user input.
- Backend: Flask, a lightweight web framework in Python, is likely used to handle user requests, interact with the database, and manage application logic.
- Database: SQLAlchemy, an object-relational mapper (ORM), simplifies interaction with the database by providing an object-oriented interface.
- Machine Learning: YOLOv8, a real-time object detection model, is likely employed for features like restricted zone enforcement, fire detection, and potentially gear detection.
- MediaPipe: This open-source framework might be used for pose estimation in the emergency alert system.
- OpenCV: This open-source computer vision library is likely used for image and video processing tasks like frame capturing, object detection bounding boxes, and motion amplification.
Running the application typically involves:
- Installing required dependencies using
pip install -r requirements.txt. - Setting up the database (likely SQLite in this case) and creating the necessary tables as defined by the models.
- Running the application using
python app.py.
Once running, users can access the web interface and leverage the provided functionalities to enhance industrial safety.