Fine-grained image recognition of Cratargi Fructus from different origin based on multi-scale feature deep neural network
Traditional Chinese medicine(TCM)is the primary approach for treating diseases and is also the he foundation for the development and innovation of TCM,the authenticity of TCM directly impacts the clinical efficacy.Therefore,scientific,reasonable,and efficient quality detection of TCM is a press-ing research topic.Cratargi Fructus(CF)as a well-known edible food in China,which has been widely used for the ability of protecting cardiovascular and lowering blood pressure in cooking and treatment.However,it is reported that the difference in natural environment and cultivation conditions affects the CF's quality and CF from different origins are easily confused,thus,the species authentication is neces-sary.Although physicochemical,biological,and manual identification methods are widely used,they have a high professional threshold and are inefficient.Image processing methods are easily affected by environmental factors,which reduces their reliability.Thus,there is an urgent need to study fast and accurate methods for the identification of CF.Inspired by CoAtNet and Swin-Transformer networks,we have proposed a hybrid neural network model with multi-scale features,combining the local information of the deep separable convolution network in MBConv and the non-local loss of the multi-level structure in Swin Transformer.By acquiring different features,the superficial features including shape,color and texture as prior knowledge have fused the high-level semantic information.A fast and effective recogni-tion method is developed to realize the effective identification of CF from different origin.Furthermore,a new global spatial attention mechanism is introduced,which can focus and learn the fine-grained fea-tures of images by forming a new structure of channel attention module and spatial attention module.Our experimental results demonstrate that our proposed method has the highest identification accuracy of 89.306％,which outperforms other baseline models.This study highlights the potential for impro-ving the scientific and technological level of TCM identification and broadening research on TCM.
国家科技期刊平台
NSTL
万方数据
维普
