The application discloses an animal
cell adaptive tracking method and
system based on
deep learning, and belongs to the technical field of
cell image analysis; a YOLOv10
deep learning model is combined with a Btrack
adaptive tracking algorithm, animal
cell samples are first dyed and microimage collection is carried out, a cell detection model is trained after image preprocessing,
data labeling and augmentation, the model is converted into an ONNX format to realize efficient
inference; then, Kalman filtering prediction and three-round Hungarian
algorithm matching are carried out to complete cell
adaptive tracking, the problems of cell
occlusion, division, overlap and loss of tracking can be effectively handled, and stable and continuous cell motion trajectories are generated; the application can be deployed without GPU, has strong generalization ability, high detection and tracking precision, is suitable for scenes such as microecological research, clinical sample testing,
drug effect evaluation and cell behavior analysis, and can significantly improve the
automation level and stability of animal cell detection, counting and dynamic tracking.