This work proposes efficient multi-scale object detection model with space-to-depth convolution and BiFPN combined with FasterNet (ES-BiCF-YOLOv8), a deep learning method, to address the problems associated with detecting steel surface defects in contemporary industrial production. The method makes innovative improvements based on the YOLOv8 algorithm and enhances the performance of the novel model mainly through the following aspects. First, the space-to-depth layer followed by a non-strided convolution layer (SPD-Conv) and the efficient multi-scale attention mechanism is introduced into the feature extraction network to enhance the model’s ability to capture fine-grained information and the fusion of multi-scale features. Second, the feature fusion network is optimized by utilizing a weighted bi-directional feature pyramid network and a lightweight network, FasterNet, to improve computational efficiency. Finally, it is shown that ES-BiCF-YOLOv8 reduces the complexity and computational requirements of the model while increasing the detection accuracy utilizing the NEU-DET dataset and deepPCB dataset with substantial experimental validation. The ES-BiCF-YOLOv8 model achieves a 5% improvement of the mean average precision value on the NEU-DET dataset, with the number of parameters and the computational amount only being the baseline 89% and 27%, and also demonstrates good generalization performance on the deepPCB dataset. Furthermore, the experiments demonstrate that ES-BiCF-YOLOv8 can be used for steel surface defect detection in industrial production because it uses less computational resources and can detect in real-time while maintaining high accuracy, in comparison to other popular object detection algorithms. The results of this work not only improve the efficiency and accuracy of steel surface defect detection but also provide ideas for the application of deep learning in the field of industrial detection.