Aiming at the problems of low positioning accuracy and slow speed of target objects in the magnetic levitation control system, a novel YOLOv5 (you only look once v5) algorithm was proposed to identify and locate the magnetic levitation ball. Firstly, by using the Mish loss function to replace the SiLU ( sigmoid-weighted linear units) activation function of YOLOv5 model, the higher accuracy and stronger generalization network model could be obtained. Then fusing the coordinate attention module into YOLOv5, the feature extraction capability of the model could be improved. On this basis, the CIOU ( complete-intersection over union) loss function was selected to replace the GIOU ( generalized intersection over union) loss function to improve the identification accuracy. Finally, the simulation verification was carried out. The results showed that the improved YOLOv5 algorithm could improve the target recognition accuracy of the magnetic levitation ball from 92. 4% to 96. 2%, and the MAP (mean average precision) from the original 88. 8% to 94. 3%. Therefore, the effectiveness and feasibility of the proposed method could be verified.