Deep learning-based laboratory safety early warning

Abstract

To address the challenges of diverse target poses, small object scales and complex background interference in laboratory hazardous behaviour detection, we propose BC-YOLOv10-S, an efficient visual recognition model for laboratory safety applications. The model introduces Shape-IoU to improve bounding-box regression, incorporates CBAM to enhance perception of critical hazardous regions, and adopts StarNet to reduce model complexity. Compared with the original YOLOv10 network, BC-YOLOv10-S improves Precision, Recall, F1-score and mAP by 7.42%, 6.94%, 7.18% and 4.62%, respectively. These results show that BC-YOLOv10-S delivers strong accuracy, robustness and lightweight deployment capability for laboratory safety warning, with clear engineering value and promising practical potential, thereby supporting the intelligent upgrading of laboratory safety management.