Vision Transformers for Helmet Compliance Monitoring: A DETR-Driven Framework for Occupational Safety

Abstract

Ensuring safety in high-risk environments like industrial zones and construction zones is a basic requirement. Here, the use of helmets is a mandatory protocol to protect the workers from head injuries. But even if strict rules have been complied with in these industrial areas, real-world compliance is inconsistent due to negligence and ignorance. Traditional methods of surveillance-based monitoring are both resource-intensive as well as can be easily prone to human oversight. This research proposes a transformer-based deep learning solution using DETR (DEtection TRansformer) for automated helmet detection. This aims to modernise and automate safety compliance monitoring systems. The proposed system leverages DETR, a novel object detection architecture that combines convolutional neural networks with Vision Transformers to eliminate the need for region proposal networks and non-maximum suppression. We calibrate DETR on a publicly available Kaggle dataset consisting of 764 images annotated in PASCAL VOC format across two classes: With Helmet and Without Helmet. The model was trained using the PyTorch framework, with strategic data augmentation applied to enhance generalisation. Performance was evaluated using mean Average Precision (mAP), precision, recall, and inference time. The DETR-based model achieved promising results in distinguishing helmet usage, delivering high accuracy and reliable localisation even with a relatively small dataset. The proposed attention-based model achieved competitive results in hard cases, where it performed better than the traditional CNN-based approaches. The study demonstrates that transformer-based object detection systems are successful in meeting real-world safety compliance tasks. The DETR architecture provides a scalable intelligence-based system for real-time helmet monitoring and can be used in automated surveillance systems in various industrial scenarios.