MSDA-Yolov8 detection algorithm for recognition of traditional Chinese medicine decoction pieces
In response to the current issues of low accuracy and inaccurate detection in traditional network models for traditional Chi-nese medicines lices,this paper proposes an optimized and improved MSDA-YOLOv8 model for detecting Chinese medicine slices based on YOLOv8n.First,SCConv is used on the Backbone to replace some C2f module,and use DyCAConv replaces some Conv.Additional-ly,a DilateBlock module is added to enhance information in the features,which improves the models feature fusion capability.On the Neck,a new C2fMSDA module is designed to replace C2f,introducing Inception block to expand the feature's receptive field.The BiFPN concept is employed for efficient bidirectional cross-scale connections and weighted feature fusion,improving network performance.Fi-nally,the MPDIoU boundary loss function is used to replace basic loss function to improve the network's bounding box regression perform-ance.Experimental results show that the improved model performs better in the dataset,with an increase of 0.7％in precision and 2.9％in mean average precision(mAP)compared to the original model.The model's parameter size is reduced by 1.9％compared to the origi-nal model.In summary,this model simultaneously reduces the model's parameter size while improving detection accuracy,significantly outperforming the comparison algorithms.It also meets the requirements of edge computing devices,demonstrating practical application value.
YOLOv8Chinese herbal medicine decoction piecesMSDAMPDIoUC2fMSDA
万方数据
维普
