Enhanced Laboratory Safety Education Through Interactive Applications of Machine Learning-Boosted Image Processing Technologies.
- Resource Type
- Article
- Authors
- Lijiao Zhang; Jianzhen Wu; Jinxin Wei; Xiaoyan Yu; Jie Yu; Bo Yuan
- Source
- Traitement du Signal. Dec2023, Vol. 40 Issue 6, p2623-2633. 11p.
- Subject
- *SAFETY education
IMAGE processing
DATA augmentation
LABORATORY management
LABORATORY safety
MACHINE learning
- Language
- ISSN
- 0765-0019
Laboratory safety education is fundamental to the smooth conduct of scientific research. Traditional educational models are often limited in interactivity and real-time feedback and they struggle to satisfy the increasing needs of laboratory safety management. This study aims to enhance the interactivity and efficacy of laboratory safety education by adopting machine learning-enhanced image processing technologies. To cope with the noise issue in hazardous behavior data within laboratories, wavelet threshold denoising is applied to significantly improve data usability. To deal with the imbalance in data samples of laboratory hazard behaviors, an adaptive boundary data augmentation algorithm is introduced to balance the dataset and strengthen the model's generalization capability. A breakthrough in extracting spatio-temporal features of hazardous behaviors in laboratories is achieved through an improved Spatio-Temporal Graph Convolutional Network (STGCN) model, enabling effective recognition and classification of hazardous behaviors. The outcome attained in this study has important implications for enhancing the interactivity and practicality of laboratory safety education and it can expand new research directions for the application of machine learning in the field of image processing. [ABSTRACT FROM AUTHOR]