Real-Time Helmet Detection System

The Real-Time Helmet Detection System is an innovative project designed to enhance rider safety by providing self-awareness reminders to wear helmets while riding motorcycles. This system utilizes advanced technologies such as Deep Learning algorithms and Tensorflow to analyze images captured by a Raspberry Pi camera in real-time. By detecting whether a rider is wearing a helmet or not, the system can promptly notify the individual through an alarming sound if a helmet is not found.

Key Features

• Real-time analysis: The system can analyze images as they are being captured, providing immediate feedback to the rider.
• Self-awareness reminders: The system recognizes if the rider is wearing a helmet and alerts the individual to wear one if not detected.
• Advanced technology: Deep Learning algorithms and Tensorflow are used to develop an accurate image classifier for helmet detection.
• Raspberry Pi integration: The model is deployed on a Raspberry Pi, making the system portable and suitable for real-time usage.
• Image preprocessing: Collected images of riders with and without helmets are cleaned and augmented to create a robust training dataset.
• Model training: A Convolutional Neural Network (CNN) model is built on top of MobileNet V2 architecture and trained using the prepared dataset.
• Testing and validation: The trained model is validated, and necessary changes and improvements are made for optimal performance.
• Raspberry Pi setup: The Raspberry Pi is equipped with a camera module and a speaker module for capturing real-time video and generating sound alerts.
• Confidence threshold: The system produces an alarm sound if the confidence level of the helmet detection exceeds 60%.

Getting Started

To replicate or further develop the Real-Time Helmet Detection System, follow these steps:

Data Collection and Preprocessing

1. Collect a diverse set of images featuring riders both wearing and not wearing helmets from various angles.
2. Preprocess the collected data by cleaning the images and applying augmentation techniques for a more comprehensive training dataset.
3. Split the data into a training set (80%) and a testing set (20%) to evaluate the model's performance.

Model Development and Training

1. Build a Convolutional Neural Network (CNN) model using Tensorflow, leveraging the MobileNet V2 architecture as a base.
2. Train the model using the prepared training dataset, adjusting hyperparameters as necessary.
3. Validate the trained model, assessing its accuracy and making any required modifications for optimization.
4. Save the trained model to the local storage for later use.

Raspberry Pi Integration and Setup

1. Set up a Raspberry Pi with a camera module and a speaker module for real-time video capture and sound generation.
2. Install the necessary packages on the Raspberry Pi, including TensorFlow, OpenCV, and NumPy.
3. Import the saved trained model to the Raspberry Pi.
4. Load the required libraries and initialize the model on the Raspberry Pi.

Real-Time Helmet Detection

1. Utilize the OpenCV library to capture real-time video frames using the Raspberry Pi camera module.
2. Feed the captured frames into the loaded model for helmet detection.
3. Examine the output of the model, and if the confidence level exceeds 60%, produce an alarm sound using the speaker module.
4. Continuously monitor and analyze the video feed for helmet detection throughout the rider's journey.

Project Benefits

The Real-Time Helmet Detection System addresses the issue of riders forgetting or neglecting to wear helmets while riding motorcycles. By providing immediate feedback and reminders, the system aims to reduce injuries and prevent fatalities caused by careless helmet non-compliance. The integration of advanced technologies, such as Deep Learning and real-time image analysis, ensures accurate and reliable helmet detection, making the system a valuable tool for rider safety.

Future Improvements

• Enhance the model's performance by exploring other advanced Deep Learning architectures and algorithms

• Expand the dataset to include a wider variety of rider profiles, lighting conditions, and environmental factors for improved generalization.

• Implement a real-time monitoring system that sends notifications to the rider's smartphone or smartwatch in addition to the audio alarm.

• Incorporate additional safety features, such as detecting other potential hazards on the road, to create a comprehensive rider safety system.

• Explore the possibility of integrating the system with existing motorcycle technologies, such as on-board diagnostics, to provide a more holistic safety solution.

• Conduct extensive field testing and gather user feedback to further refine and optimize the system's performance and usability.