AI-Driven Automated Helmet Detection in Underground Coal Mines using Attention-Enhanced Vision Transformer - Yasin, Muhammad; Smarandache, Florentin; Waheed Sabir, Muhammad; Arslan, Farrukh; Waqas, Muhammad (Engineering, Technology & Applied Science Research, 2025)
Ensuring safety compliance in underground coal mines is essential for preventing accidents and safeguarding miners. Traditional methods for monitoring helmet usage are often ineffective due to poor visibility, dust, and equipment occlusion. This study proposes an attention-enhanced Vision Transformer (ViT) model, specifically adapted for helmet detection in challenging underground environments. The model processes images as sequences of patches, leveraging multi-head self-attention mechanisms to capture global dependencies and improve feature extraction. A custom dataset was developed from underground coal mine footage, and the model was trained using supervised learning with a cross-entropy loss function. The customized ViT achieved an accuracy of 98%, outperforming other State-Of-The-Art (SOTA) models, such as YOLOv8 with attention mechanisms, Mask R-CNN, and Detectron2. The results demonstrate the effectiveness of the attention-enhanced ViT in accurately detecting helmets, even in low-light and cluttered environments. This research contributes to developing real-time, automated safety monitoring systems, which reduce human error and enhance worker safety in hazardous mining operations.