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.

Description: This dataset has been curated to support research in bone fracture classification, specifically focusing on simple and comminuted fractures. It includes high-quality X-ray images to aid in the development and evaluation of machine learning models for medical imaging applications. This dataset is ideal for image classification, segmentation, and fracture type recognition tasks.

Dataset Overview: The dataset contains X-ray images of bones affected by two types of fractures:

Simple Fracture: Images exclusively sourced from hospital records. Original: 164 images, augmented to 3,280 images.

Comminuted Fracture: A mix of hospital-sourced images and web-sourced images (approximately one-third from web pages). Original: 145 images, augmented to 2,900 images. The dataset has been updated to provide a larger variety of augmented images using additional augmentation techniques. It now includes a total of 6,489 augmented images, ensuring more comprehensive coverage of potential fracture patterns.

Key Features:

Number of Original Images: 309
Number of Augmented Images: 6,489
Total Dataset Size: 6,798 images (Original + Augmented)
File Formats: JPG, PNG
Augmentation Techniques: High brightness, low brightness, high contrast, low contrast, rotations, and varied orientations.

Update Details (Version 2): Original Submission Date: 15 Nov 2024 Update Date: 23 Nov 2024

Changes in This Version: The dataset size has been increased with additional augmented images (from 3,090 to 6,489 augmented images). Updated augmentation techniques to include more diverse transformations.

Improved class balance: Simple Fracture: Increased from 1,640 to 3,280 augmented images. Comminuted Fracture: Increased from 1,450 to 2,900 augmented images.

Applications: 1.Medical Imaging: Useful for training models in fracture classification and identification. 2.Healthcare Technology: Supports the development of diagnostic tools and mobile applications for real-time fracture detection. 3.Medical Research: Assists in understanding fracture patterns and their visual indicators.

Dataset Collection: The simple fracture images were exclusively sourced from hospitals, ensuring clinical relevance. The comminuted fracture images were gathered from a combination of hospital and web sources, with approximately one-third originating from web pages. This dataset aims to provide realistic scenarios for training models in a clinical setting, incorporating diverse lighting conditions and image orientations to simulate various imaging environments.

Over 70% of organic halogens, representing chlorine- and bromine-containing disinfection byproducts (Cl-/Br-DBPs), remain unidentified after 50 years of research. This work introduces a streamlined and cloud-based exposomics workflow that integrates high-resolution mass spectrometry (HRMS) analysis, multistage machine learning, and cloud computing for efficient analysis and characterization of Cl-/Br-DBPs. In particular, the multistage machine learning structure employs progressively different heavy isotopic peaks at each layer and capture the distinct isotopic characteristics of nonhalogenated compounds and Cl-/Br-compounds at different halogenation levels. This innovative approach enables the recognition of 22 types of Cl-/Br-compounds with up to 6 Br and 8 Cl atoms. To address the data imbalance among different classes, particularly the limited number of heavily chlorinated and brominated compounds, data perturbation is performed to generate hypothetical/synthetic molecular formulas containing multiple Cl and Br atoms, facilitating data augmentation. To further benefit the environmental chemistry community with limited computational experience and hardware access, above innovations are incorporated into HalogenFinder (http://www.halogenfinder.com/), a user-friendly, web-based platform for Cl-/Br-compound characterization, with statistical analysis support via MetaboAnalyst. In the benchmarking, HalogenFinder outperformed two established tools, achieving a higher recognition rate for 277 authentic Cl-/Br-compounds and uniquely identifying the number of Cl/Br atoms. In laboratory tests of DBP mixtures, it identified 72 Cl-/Br-DBPs with proposed structures, of which eight were confirmed with chemical standards. A retrospective analysis of 2022 finished water HRMS data revealed insightful temporal trends in Cl-DBP features. These results demonstrate HalogenFinder’s effectiveness in advancing Cl-/Br-compound identification for environmental science and exposomics.