Helmet detection ensures safety in industries like construction, manufacturing and bike driving. This technology uses advanced image processing and machine learning algorithms to identify whether an individual is wearing a helmet. By preventing head-related accidents, helmet detection promotes a culture of safety among employees and helps companies comply with safety regulations. This study, based on the You Only Look Once model (YOLO) for helmet detection, uses transfer learning to train the model on a dataset of helmet images and fine-tune it to detect helmets. This approach involves starting with a pre-trained YOLO model on a large general object detection dataset and then, fine-tuning the model on a smaller dataset of helmet images. It is highly accurate and effective in handling complex lighting conditions and perspectives, making it less sensitive to image orientation and lighting conditions compared to traditional methods. YOLO is also scalable and adaptable. Experimental results demonstrate the method is effective in helmet detection in terms of precision, recall and mAP metrics.
