Microbial cell recognition algorithm based on improved YOLOv7
It is time-consuming when the traditional and manual methods are used to identify multiple species and large quantity of microbial cells,and the identification accuracy is low.In view of this,a microbial cell recognition algorithm named YOLOv7-PN based on the improved YOLOv7 is proposed.The improved path aggregation network(PANet)is introduced to enable the extraction and fusion of features at different scales in order to facilitate the capture of multi-scale information in cell images,which enhances its detection accuracy and robustness of cells.The incorporation of NAM into the backbone network enables the adaptive learning of weights for each channel,which enhances the feature representation of cells.Furthermore,the replacement of the traditional IoU bounding box loss function with DIoU_Loss allows for the consideration of the distance and overlap between bounding boxes,thereby facilitating more accurate precision measurement,which,in turn,enhances the accuracy of cell localization.The experimental results demonstrate that the algorithm presented in this paper exhibits a notable enhancement in the capacity to recognize microbial cells when evaluated with the BCCD dataset.In comparison to the benchmark algorithm YOLOv7,the YOLOv7-PN demonstrates an improvement.Its precision is increased by 1.46%,its F1 by 2.61%and its accuracy rate by 0.86%.The experimental results demonstrate the efficacy and superiority of the algorithm.Therefore,the algorithm can provide compelling evidence for its utility in the microbial cell analysis in microbiology research and medical diagnosis,as well as in other fields.
万方数据
维普
