Object detection algorithm and graph convolutional network for graded early warning of laboratory misconduct

Abstract

With the increasing demand for laboratory safety management, precise detection and graded early warning of misconduct become more critical. Traditional manual monitoring methods suffer from low efficiency, limited coverage, and difficulty handling complex scenarios. To address this, this paper proposes a laboratory misconduct graded early warning model based on object detection algorithms and Graph Convolutional Networks. The model uses You Only Look Once version 9, combined with Convolutional Block Attention Mechanisms, to enhance key feature extraction and accurately identify misconduct. Meanwhile, the Graph Convolutional Network explores spatial correlations between behaviors, and gated recurrent units capture temporal dynamic features to implement graded risk warning. The experimental evaluation showed a minimum loss of 0.027 after 120 iterations, demonstrating superior performance compared with the comparison models, which recorded loss values of 0.24, 0.25, and 0.32. In graded early warning tests, the model reaches an accuracy of 95.62%, with precision and recall exceeding 92%, clearly higher than the highest values of comparison models at 88.21% and 88.01%. These results indicate that the model can achieve precise detection and graded early warning of laboratory misconduct, providing an intelligent solution for laboratory safety management and promoting efficient and accurate safety monitoring.