Detection of Cells and Microbes in Microscopic Field Based on Improved YOLOv5

The detection of cell and microbes under the microscope is of great value in both clinical experiments and experimental teaching. However, the narrow field of view of conventional light microscopes and the problem of cell or microbial stacking make target detection a challenging task. In this paper, the YOLOv5 target detection method is improved through the attention mechanism, so that it can realize the target detection of cells and microorganisms. The Efficient Channel Attention (ECA) module is added to the YOLOv5 model to extract key features, and we also replace the Path Aggregation Network (PANet) of YOLOv5 with Bidirectional Feature Pyramid Network (BiFPN) for fast multi-scale feature fusion. The average precision (AP@0.5) of the improved algorithm in this paper is 81.98% under the cell and microbe microscopy datasets, which is 1.95% higher than the YOLOv5s model. The model is significantly better than the traditional deep learning algorithm, and can be effectively used for the detection of cells and microorganisms under the light microscope.