The document discusses developing a system to automatically detect if individuals are wearing helmets using machine learning. It begins with an introduction on the importance of helmet safety. It then outlines the problem statement, literature review on existing methods achieving 80-92% accuracy, objectives of achieving high detection accuracy while addressing privacy and cost-effectiveness, methodology, and tools like Python and data annotation programs.

1. U
HELMET DETECTION USING
MACHINE LEARNING

2. TABLE OF
CONTENT
1.INTRODUCTION
2.PROBLEM
STATEMENT
3.LITERATURE
REVIEW
4. OBJECTIVES
5.METHODOLOGY
6.TOOLS

3. INTRODUCTION
•Introduction: The use of helmets is crucial for safety in various
activities, such as riding motorcycles, bicycles, and in construction
industries.
•Motivation: To develop a system that can automatically detect
whether individuals are wearing helmets, which can be useful for
enhancing safety measures and enforcing helmet-wearing policies.

4. PROBLEM STATEMENT
HELMET DETECTION
USING MACHINE LEARNING

5. Literature Review
Paper Title Year Accuracy Technique
Method - I A Review on
Helmet Detection by
using Image Processing
and Convolutional Neural
Networks
2019 92% Image Processing, Convolutional Neural
Networks and Support Vector Machine
Method - II Detection of
Motorcyclists without
Helmet in Videos using
Convolutional Neural
Network
2019 92.87% Gaussian mixture model and
Convolutional Neural Networks
Method - III Automatic
Helmet Detection System
on Motorcyclists Using
YOLOv3
2020 80% YOLOv3 and Region of Interest

6. OBJECTIVES:
Achieve high accuracy in
detecting whether a person
is wearing a helmet or not.
This involves training a
machine learning model that
can effectively distinguish
between helmeted and non-
helmeted individuals.
Address privacy concerns by
implementing measures to
anonymize or securely
handle captured images or
videos. Compliance with
privacy regulations and
standards is essential.
Privacy Considerations
Optimize the system for
cost-effectiveness,
considering factors such as
hardware requirements,
energy consumption, and
maintenance costs.
Detection Accuracy Privacy Considerations Cost-effectiveness

7. METHODOLOGY

8. TOOLS/PLATFORMS
• PROGRAMMING LANGUAGE: PYTHON
• DATA STRUCTURE CONCEPTS: LIST, ARRAY
• DATA ANNOTATION: LABELIMG, LABELBOX