Aiming at the problems of less research on the detection of pathogenic microorganisms

large differences in target size and complex background

a multi-scale detection method of pathogenic microorganisms fused with recursive feature pyramid was proposed. Considering that the characteristics of some pathogenic microorganisms are similar to normal cells

introducing the mixed attention module CBAM in the feature extraction stage of YOLOv5 can enhance the significance of pathogenic microorganisms and reduce the misdiagnosis rate of pathogenic microorganisms. Since the pathogenic microorganisms with small size will lose important features in the downsampling process

the recursive feature pyramid RFP is used in the feature fusion stage and the detection layer of the small target is added. The detection rate of the target is improved

and the depthwise separable convolution DSConv is used in the secondary feature extraction module to replace the ordinary convolution Conv

which reduces the number of parameters of the model and improves the performance. Experiments show that the mAP of the algorithm in this paper on the pathogenic microorganism dataset has reached 74. 9%

which is 4. 9% higher than the baseline network YOLOv5

and the mAP of the small target Trichomonas has alsoincreased by 6. 2% . At the same time

the public datasets of PascalVOC2007

PascalVOC2012 and CDetector are improved by 1. 9%

3. 1% and 8% respectively compared with the baseline network YOLOv5.