In the transformation and upgrade of special steel enterprises into “lighthouse factories”, real-time tracking of steel pipe materials is a core component. Due to the diversity of materials and the complexity of the production line, proximity sensors fail to meet the reliability requirements of material detection. Therefore, according to the existing environment and requirements of the workshop, a material tracking camera system is built, and the image data set composed of some characteristics of materials and production lines is collected. Based on video analysis, a steel pipe target detection algorithm for real-time material tracking in special steel workshops is introduced. The algorithm is based on the PPYOLOE network. Firstly, the CSPRepResNet backbone in PPYOLOE is replaced with the lightweight HGNetV2 backbone, which enhances feature extraction capabilities while reducing the number of parameters. Secondly, HG-Block and SPPELAN are integrated into the Neck, further reducing the parameters and improving speed. Finally, in the upsampling stage, the Dysample dynamic upsampling operator is employed to enhance the fusion of multi-scale features, thus improving detection accuracy. Experimental results show that compared with the original PPYOLOE algorithm, the improved algorithm enhances detection accuracy by 1.6%, reaching 80.5%, and increases detection speed by 16%, reaching 56.4 FPS, while GFlops and parameters are reduced by 35% and 33%, respectively. The improved algorithm effectively boosts both detection accuracy and speed,and through on-site deployment, it meets the real-time tracking requirements of steel pipe materials.