Research on Chemical Experiment Instrument Detection Based on the Improved ResNet50-fpn Algorithm

With the increasing frequency of use of chemical experimental instruments, there is a growing need for efficient instrument management and fault diagnosis. To address this, this paper proposes an improved detection method based on the ResNet50-FPN (Feature Pyramid Network) algorithm. By combining Deep Convolutional Neural Networks (CNN) with FPN's multi-scale feature fusion, the method enhances the accuracy and robustness of instrument detection. The ResNet50 model is optimized by refining residual connections and interlayer feature transfer, boosting its ability to capture detailed features. The integration of FPN enables the model to effectively extract multi-scale features, improving instrument recognition in complex backgrounds and across varying sizes. Experimental results show that the proposed method outperforms traditional detection approaches in both accuracy and recall, demonstrating strong application potential. This research offers valuable insights for the intelligent detection of chemical instruments, particularly in laboratory automation, fault diagnosis, and instrument management.