In the clinical stage, suspected hemolytic plasma may cause hemolysis illness, manifesting as symptoms such as heart failure, severe anemia, etc. Applying a deep learning method to plasma images significantly improves recognition accuracy, so that this paper proposes a plasma quality detection model based on improved “You Only Look Once” 5th version (YOLOv5). Then the model presented in this paper and the evaluation system ?were introduced? into the plasma datasets, and ?the average accuracy of the final classification reached 98.7%?. The results of this paper's experiment were obtained through the combination of several key algorithm modules including? omni-dimensional dynamic convolution, pooling with separable kernel attention, residual bi-fusion feature pyramid network, ?and? re-parameterization convolution. The method of this paper? obtains the feature information of spatial mapping efficiently, and enhances the average recognition accuracy of plasma quality detection. This paper presents a high-efficiency detection method for plasma images, aiming to provide a practical approach to prevent hemolysis illnesses caused by external factors.