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DATA SCIENCE
INTERNSHIP
AT TATA INSIGHTS AND QUANTS
BY AYUSH SINGH (209309034)
Abstract
This presentation is an overview of the data science internship
conducted at Tata Insights and Quants, focusing on the critical tasks
of Personal Protective Equipment (PPE) detection and Fire Accident
detection.
The primary objective was to develop robust and accurate computer
vision models to enhance safety measures within the workplace.
Problem Statement
Tata Industries were facing a lot of challenges handling workplace
accidents due to not wearing proper equipment and fire casualties.
Thesechallenges included accurate monitoring of Personal Protective
Equipment (PPE) usage, leading to potential lapses in compliance.
The lack of advanced technological solutions left the company reliant
on traditional methods, contributing to inefficiencies and an increased
likelihood of safety breaches.
Solution
To solve the problem the team decided to devise a Computer Vision
Algorithm capable of real time detection of all the PPE equipment
like Helmets, Vests, Gloves and Footwear.
The Model should be robust and with high inference speed.
METHODOLGY
1) Data Collection
2) Data Labelling
3) Model Architecture Selection
4)Performance Evaluation
5)Deployment
Data Collection
As high inference speeds were required, Custom Datasets were used
but results were not at par.
Thus, The team was advised to use the real time images of the
workers and even collect images from DSLR Cameras.
The collected images were combined as whole new dataset and the
model was trained on them.
Data Labelling
As we were using a Supervised Learning Algorithm, The images
needed to be accurately labelled.
Thus LabelImg and YOLO V8 were used for proper labelling of the
images.
LabelImg is used for manual labelling of Images.
YOLO V8 is a new model for automatic image labelling
LabelImg
Manual Labelling through
LabelImg.
Model Architecture Selection
The Model Architecture was the most important part.
Inferences were compared between state-of-the-art architectures
like Vision Transformer, YOLO V5, V6, V7 and CNNs.
The final model was an integration of the above-mentioned
architectures.
Implementation and Result
The implementation yielded highly promising results with an
achieved accuracy of 93%, demonstrating the effectiveness of the
developed solution in accurately detecting PPE adherence and
potential fire accidents within the workplace.
The real-world applicability of the model was validated through
comprehensive performance evaluations across diverse scenarios and
test datasets.
The model demonstrated a high level of accuracy in identifying
individuals adhering to PPE guidelines and swiftly detecting signs of
fire-related incidents.

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Project Report_AyushSingh_209309034.pptx

  • 1. DATA SCIENCE INTERNSHIP AT TATA INSIGHTS AND QUANTS BY AYUSH SINGH (209309034)
  • 2. Abstract This presentation is an overview of the data science internship conducted at Tata Insights and Quants, focusing on the critical tasks of Personal Protective Equipment (PPE) detection and Fire Accident detection. The primary objective was to develop robust and accurate computer vision models to enhance safety measures within the workplace.
  • 3. Problem Statement Tata Industries were facing a lot of challenges handling workplace accidents due to not wearing proper equipment and fire casualties. Thesechallenges included accurate monitoring of Personal Protective Equipment (PPE) usage, leading to potential lapses in compliance. The lack of advanced technological solutions left the company reliant on traditional methods, contributing to inefficiencies and an increased likelihood of safety breaches.
  • 4. Solution To solve the problem the team decided to devise a Computer Vision Algorithm capable of real time detection of all the PPE equipment like Helmets, Vests, Gloves and Footwear. The Model should be robust and with high inference speed.
  • 5. METHODOLGY 1) Data Collection 2) Data Labelling 3) Model Architecture Selection 4)Performance Evaluation 5)Deployment
  • 6. Data Collection As high inference speeds were required, Custom Datasets were used but results were not at par. Thus, The team was advised to use the real time images of the workers and even collect images from DSLR Cameras. The collected images were combined as whole new dataset and the model was trained on them.
  • 7. Data Labelling As we were using a Supervised Learning Algorithm, The images needed to be accurately labelled. Thus LabelImg and YOLO V8 were used for proper labelling of the images. LabelImg is used for manual labelling of Images. YOLO V8 is a new model for automatic image labelling
  • 9. Model Architecture Selection The Model Architecture was the most important part. Inferences were compared between state-of-the-art architectures like Vision Transformer, YOLO V5, V6, V7 and CNNs. The final model was an integration of the above-mentioned architectures.
  • 10. Implementation and Result The implementation yielded highly promising results with an achieved accuracy of 93%, demonstrating the effectiveness of the developed solution in accurately detecting PPE adherence and potential fire accidents within the workplace. The real-world applicability of the model was validated through comprehensive performance evaluations across diverse scenarios and test datasets. The model demonstrated a high level of accuracy in identifying individuals adhering to PPE guidelines and swiftly detecting signs of fire-related incidents.