Deep Learning-Based Workers Safety Helmet Wearing Detection on Construction Sites Using Multi-Scale Features

Deep Learning-Based Workers Safety Helmet Wearing Detection on Construction Sites Using Multi-Scale Features

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Product Code: Python - Deep Learning
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Product Description

Abstract:

            Due to a lack of knowledge about safety helmets, accidents and injuries on construction sites are now increasingly common. Worker supervision by hand is challenging and ineffective. Workers often take off the helmets because of weak security-conscious and discomfort, then hidden dangers will be brought by this behaviour. Workers without safety helmets will suffer more injuries in accidents such as falling human body and vertical falling matter. Hence, detecting safety helmet wearing is a vital step of construction sites safety management and a safety helmet detector. However, traditional manual monitor is labour intensive and methods of installing sensors on safety helmet are difficult to popularize. This study is visually checking the construction site to see if anyone is wearing a safety helmet. In order to recognise a safety helmet in real time at a building site, we built a deep learning-based technique.

Proposed System:

            In proposed system, we have used YOLOv5 to detect the object. YOLOv5 is the latest and the lightweight version of previous YOLO algorithms and uses Focus structure with CSPdarknet53 as a backbone. The Focus layer is first introduced in YOLOv5. The Focus layer replaces the first three layers in the YOLOv3 algorithm.YOLOv5 uses auto learning bounding boxes which improves the overall accuracy of the algorithm that can detect objects more accurately compared to YOLOv3.      

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Software Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
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  7. Source Code
  8. Screen Shots
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The Delivery time for software projects is 2 -3 working days. Some of the software projects will require Hardware interface. Please go through the hardware Requirements in the abstract carefully. The Hardware will take 7-8 Working Days

 

Hardware Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. Datasheets
  6. Circuit Diagrams
  7. Source Code
  8. Screen Shots & Photos
  9. Software Links
  10. Reference Papers
  11. Lit survey
  12. Full Project Documentation
  13. Online support


The Delivery time for Hardware projects is 7-8 working days.

   

Mini Projects: Software Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support

 

The Delivery time for software Miniprojects is 2 -3 working days.

 

Mini Projects - Hardware includes

  1. Demo  Video
  2. Abstract
  3. PPT
  4. Datasheets
  5. Circuit Diagrams
  6. Source Code
  7. Screen Shots & Photos
  8. Software Links
  9. Reference Papers
  10. Full Project Documentation
  11. Online support

The Delivery time for Hardware Mini projects is 7-8 working days.