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Single cell dispensing techniques mainly include limiting dilution, fluorescent-activated cell sorting (FACS) and microfluidic approaches. Limiting dilution process is complicated by statistical analysis of clonally derived cell lines. Flow cytometry and conventional microfluidic chip methods utilize excitation fluorescence signals for detection, potentially causing a non-negligible effect on cell activity. In this paper, we implement a nearly non-destructive single-cell dispensing method based on object detection algorithm. To realize single cell detection, we have built automated image acquisition system and then employed PP-YOLO neural network model as detection framework. Through architecture comparison and parameter optimization, we select ResNet-18vd as backbone for feature extraction. We train and evaluate the flow cell detection model on train and test set consisting of 4076 and 453 annotated images respectively. Experiments show that the model inference an image of 320 × 320 pixels at least 0.9 ms with the precision of 98.6% on a NVidia A100 GPU, achieving a good balance of detection speed and accuracy.