Model training parameters.

The identification research of hydrogenation catalyst information has always been one of the most important businesses in the chemical industry. In order to aid researchers in efficiently screening high-performance catalyst carriers and tackle the pressing challenge at hand, it is imperative to find a solution for the intelligent recognition of hydrogenation catalyst images. To address the issue of low recognition accuracy caused by adhesion and stacking of hydrogenation catalysts, An image recognition algorithm of hydrogenation catalyst based on FPNC Net was proposed in this paper. In the present study, Resnet50 backbone network was used to extract the features, and spatially-separable convolution kernel was used to extract the multi-scale features of catalyst fringe. In addition, to effectively segment the adhesive regions of stripes, FPN (Feature Pyramid Network) is added to the backbone network for deep and shallow feature fusion. Introducing an attention module to adaptively adjust weights can effectively highlight the target features of the catalyst. The experimental results showed that the FPNC Net model achieved an accuracy of 94.2% and an AP value improvement of 19.37% compared to the original CenterNet model. The improved model demonstrates a significant enhancement in detection accuracy, indicating a high capability for detecting hydrogenation catalyst targets

加氢催化剂信息识别研究一直是化工行业最为重要的业务方向之一。为助力研究人员高效筛选高性能催化剂载体，解决当前面临的迫切难题，亟需构建加氢催化剂图像的智能识别方案。针对加氢催化剂因黏附、堆叠导致识别精度较低的问题，本文提出了一种基于FPNC Net的加氢催化剂图像识别算法。本研究采用Resnet50作为骨干网络提取特征，并利用空间可分离卷积核提取催化剂边缘的多尺度特征。此外，为有效分割条纹状黏附区域，本文在骨干网络中加入FPN（特征金字塔网络，Feature Pyramid Network）以实现深浅特征融合。引入注意力模块自适应调整权重，可有效突出催化剂的目标特征。实验结果表明，相较于原始CenterNet模型，FPNC Net模型的识别准确率达到94.2%，AP值提升了19.37%。改进后的模型检测精度得到显著提升，具备优异的加氢催化剂目标检测能力。