In order to solve the problems of low detection accuracy and weak generalization ability of steel cord surface defects, a steel cord defect detection method based on DCGAN and improved YOLOv5s was proposed. Firstly, by adjusting DCGAN network parameters and optimizing hyperparameters, the generator can generate steel cord defect images with rich features and clear texture, thus expanding the data set. Secondly, the K-Means++ algorithm is used to re-cluster the anchor frame to obtain better anchor frame parameters and achieve accurate matching between anchor frame and actual defects. Then, coordinate attention mechanism was added to C3 module of YOLOv5s backbone network to enhance the feature extraction capability and accurate localization capability of the model. Finally, MPDIoU loss function is introduced to replace YOLOv5s original loss function to further improve the detection accuracy. The experimental results show that on the measured steel cord defect data set, the average accuracy of defect detection is increased by 6.6%, reaching 89.4% by using the YOLOv5s detection model enhanced and improved by DCGAN data, and the detection accuracy and recall rate are also improved. Compared with other mainstream detection models, this model not only improves the detection speed by about 30%, but also maintains high detection accuracy. On the publicly available NEU-DET dataset, the mAP value of this model reaches 82.6%, which is 3.8% higher than that of the original YOLOv5s model.