Automated Welder Safety Assurance: A YOLOv3-Based Approach for Real-Time Detection of Welding Helmet Availability

This paper presents the development of a novel real-time monitoring and detection system designed to identify the presence of welding helmets on workers’ faces during welding activities. The system employs a Convolutional Neural Network (CNN) based on the YOLOv3 algorithm and is trained and validated using a diverse dataset that includes images with varying levels of blur, grayscale images, and drone-captured photos. The model’s effectiveness is evaluated using five key performance metrics: accuracy, precision, recall, F1 score, and the AUC-ROC curve. Additionally, the study investigates the impact of various input image sizes, batch sizes, activation functions, and the incorporation of additional convolutional layers on model performance. The results indicate that the Swish activation function, combined with a batch size of 128, an input image size of
$256\times 256$
, and the addition of one convolutional layer, yielded superior performance. The model achieved outstanding values of 98% for precision, recall, and F1 score, along with an AUC of 0.98, underscoring its accuracy and reliability in detecting welding helmets.