In order to improve the accuracy of the detection and positioning of the number of ladle carrier tank based on computer vision, reduce the detection error in the case of contamination, reduce the missing detection problem caused by the small area of the number of tank and improve the detection speed, a detection and recognition method of the number of ladle carrier tank based on improved YOLOv5 network was proposed. The feature extraction capability of the model was enhanced by adding attention mechanism into the feature extraction network. By upgrading the backbone network to lightweight GhostBottleNeck, the reasoning speed of the model is accelerated. By performing Affine Transformation on the target character, the distorted character is converted into a near-positive perspective, and then the improved ResNet network is used for single-character recognition. The results show that the accuracy of the improved network is 90.3%, the recall rate is 87.3%, and the final number identification accuracy is 97.7%, indicating that the method can effectively achieve the accurate location and identification of the number of the ladle carrier tank, and provide reliable data support for intelligent management.