# Microbial Colony Detection Based on Deep Learning
## 5 Conclusions  
In this paper, we propose a new method combining a style transfer extended microbial colony image dataset with a Swin Transformer-based Cascade Mask R-CNN detector for model training. Throughout the experiments, we use Faster R-CNN, Cascade R-CNN, Mask R-CNN, and Cascade Mask R-CNN as the architecture networks and ResNet50, HRNet, and Swin Transformer as the backbone networks to perform the model training and comparison experiments on the AGAR dataset, respectively. The training results indicate that the detector performs well in object detection in microbial colony plate images, with the most accurate being mAP = 0.614, which is 2.2% higher than that of the best performing model of the AGAR dataset experiment (Cascade R-CNN with HRNet). We input microbial colony images into the trained models and assess the detection performance of the various models by analyzing the predictions. The results demonstrate that the Swin Transformer, utilized as a feature extraction network, achieves superior detection results on the microbial colony AGAR dataset. We use YOLOv8x as a pre-trained model, and the results show a mAP of 76.7%. Compared with the two-stage object detection models in this paper, YOLOv8x has a stronger object detection performance, while the experimental results also indicate that it is outstanding in the scenario of microbial colony object detection.  
The application of style transfer in the experiments addresses the challenges of fully supervised learning, especially when dealing with imbalanced sources of plate images that might neglect colonies growing at the edges of the Petri dish. The Swin Transformer block, utilized as the model's feature extraction method, demonstrates excellent performance in the detection results, providing hierarchical features that linearly correlate with the input image. Overall, the findings suggest that the proposed method shows promise in enhancing microbial colony identification in plate images. And we intend to expand our research into additional categories of microbial colony object detection and further explore models such as Cascade Mask R-CNN and YOLOv8.  
Conceptualization, F.Y. and Y.Z.; methodology, F.Y.; software (PyCharm Community Edition 2022.3.1), F.Y.; validation, F.Y., H.Y. and Y.Z.; formal analysis, F.Y.; investigation, F.Y.; resources, Y.W.; data curation, F.Y. and H.Y.; writing--original draft preparation, F.Y.; writing--review and editing, Y.W. and Z.H.; visualization, F.Y and S.P.; supervision, Y.W. and Z.H.; project administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript. Funding was provided by the Central Government Guides Local Science and Technology Development Projects (ZY2022 HN01), the Hainan Province Science and Technology Special Fund (ZDYF2023 GXJS003), the Hainan Province Science and Technology Special Fund (ZDYF2022 XDNY246), the National Natural Science Foundation of China (NSFC) (42166001), and the Collaborative Innovation Center of Marine Science and Technology, Hainan University (XTCX2022 HYB09). Institutional Review Board StatementThis chapter does not contain any studies with human participants or animals performed by any of the authors. Informed Consent StatementNot applicable. Data Availability StatementThe datasets generated during and/or analyzed during the current study are available from the corresponding authors upon reasonable request. ConflictsThe authors declare no conflict of interest.