HelmNet

HelmNet is a computer vision system designed to detect safety helmets in industrial environments. I built the model using deep learning and convolutional neural networks to improve workplace safety monitoring.

First, I prepared and annotated image data representing workers with and without helmets. Next, I trained a convolutional neural network to recognize protective equipment across different lighting conditions and viewpoints.

In addition, I implemented data augmentation techniques to improve model robustness and generalization. The system can analyze images in real time and detect helmet usage automatically.

As a result, HelmNet provides an automated safety monitoring solution that helps organizations identify compliance issues and reduce workplace risk.

HelmNet is an end-to-end computer vision system designed to detect safety helmets in industrial environments and support automated workplace safety monitoring. I developed the model using deep learning and modern computer vision techniques.

First, I collected and prepared image datasets representing workers in industrial settings. I then performed image preprocessing and data augmentation to improve model robustness across different environments, lighting conditions, and viewing angles.

Next, I trained convolutional neural network models to detect whether workers were wearing protective helmets. The system learns visual patterns related to safety equipment and identifies compliance automatically.

In addition, I evaluated model performance using standard computer vision metrics and optimized the architecture to improve detection accuracy.

Overall, the HelmNet system demonstrates how computer vision and deep learning can improve industrial safety monitoring. The project highlights my ability to design, train, and evaluate real-world AI systems for automated detection tasks.

Stack:

Python, TensorFlow/Keras, OpenCV, CNNs, Data Augmentation

  • Designed a custom Convolutional Neural Network (CNN) to automate safety helmet detection in high-risk environments, implementing advanced data augmentation (geometric transformations) to handle diverse lighting conditions.
  • Achieved 98.5% accuracy on the test set, creating a scalable automated monitoring tool that significantly reduces manual compliance oversight.

“Vision systems should work outside the lab — the dataset and augmentation strategy matter as much as the model.”

Randley Morales, Ph.D.Ph.D. Mathematician & Machine Learning Specialist | Generative AI, Computer Vision & Predictive Modeling

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