Deep learning-based laboratory safety early warning
TL;DR
The 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.
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.