CV-CBI-MINING SAFETY Computer Vision Project

Project Description for Roboflow: Mining Safety - PPE Detection

Project Name: Mining Safety - PPE Detection

Overview:

The Mining Safety - PPE Detection project aims to enhance safety protocols in mining environments by leveraging computer vision technology to detect Personal Protective Equipment (PPE). This project focuses on the detection of various PPE items and the absence of mandatory safety gear to ensure that workers adhere to safety regulations, thereby minimizing the risk of accidents and injuries.

Objective:

To develop a robust object detection model capable of accurately identifying 13 different classes of PPE in real-time using a dataset sourced from Roboflow Universe. The ultimate goal is to integrate this model into a monitoring system that can alert supervisors about non-compliance with PPE requirements in mining sites.

PPE Classes (Labels):

1. Goggles
2. Helmet
3. Mask
4. No-Boots
5. No-Gloves
6. No-Helmet
7. No-Mask
8. No-Vest
9. Undefined
10. Vest
11. Boots
12. Ear-Protection
13. Gloves

Dataset:

• Total Images: 7444
• Source: Roboflow Universe
• Annotations: Each image is annotated with bounding boxes corresponding to one or more of the 13 PPE classes.
• Image Variety: The images come from various mining sites with different lighting conditions, camera angles, and worker positions to ensure diversity and robustness of the model.

Project Steps:

1. Data Collection and Annotation:

   • Import and utilize the dataset from Roboflow Universe, ensuring it covers diverse conditions and scenarios.
   • Verify and, if necessary, re-annotate images to match the 13 PPE classes accurately using the Roboflow platform.

2. Data Preprocessing:

   • Perform data augmentation techniques such as rotation, scaling, and cropping to increase the variability and size of the dataset.
   • Split the dataset into training, validation, and test sets (e.g., 80% training, 10% validation, 10% test).

3. Model Selection and Training:

   • Use a pre-trained YOLO (You Only Look Once) model due to its efficiency and accuracy in real-time object detection tasks.
   • Fine-tune the model on the annotated dataset using transfer learning to adapt it specifically to the mining safety PPE detection task.

4. Model Evaluation:

   • Evaluate the model's performance using metrics such as precision, recall, F1-score, and mean Average Precision (mAP).
   • Conduct error analysis to identify common misclassifications and refine the model accordingly.

5. Deployment:

   • Integrate the trained model into a real-time monitoring system.
   • Develop a user interface that displays video feeds and highlights detected PPE and any non-compliance issues.
   • Implement alert mechanisms to notify supervisors of any detected safety violations.

6. Continuous Improvement:

   • Collect feedback from the deployment to continuously improve the model.
   • Regularly update the dataset with new images and retrain the model to maintain high accuracy.

Expected Outcomes:

• A high-accuracy object detection model capable of identifying and differentiating between 13 classes of PPE.
• Enhanced safety monitoring system for mining sites, reducing the likelihood of accidents due to non-compliance with PPE regulations.
• A scalable solution that can be adapted to other industrial environments requiring PPE detection.

Tools and Technologies:

• Annotation Tool: Roboflow
• Object Detection Model: YOLO (preferably YOLOv8 or YOLOv9 for efficiency)
• Programming Language: Python
• Frameworks: PyTorch or TensorFlow for model training and inference
• Deployment Platform: Docker for containerization and deployment on edge devices or cloud platforms
• Monitoring and Alert System: Custom-built using Flask/Django (for web interface) and integrated with real-time notification services (e.g., Slack, email, SMS)

This project will significantly contribute to improving the safety standards in mining operations by ensuring that all workers are consistently wearing the required protective gear.

                        @misc{
                            cv-cbi-mining-safety_dataset,
                            title = { CV-CBI-MINING SAFETY Dataset },
                            type = { Open Source Dataset },
                            author = { MININGSAFETYCBI },
                            howpublished = { \url{ https://universe.roboflow.com/miningsafetycbi/cv-cbi-mining-safety } },
                            url = { https://universe.roboflow.com/miningsafetycbi/cv-cbi-mining-safety },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2024 },
                            month = { may },
                            note = { visited on 2024-11-21 },
                            }