The YOLOv5s-FCS network for traditional steel materials, which addresses issues such as false positives, false negatives, and low accuracy in detecting certain types of defects was presented. Firstly, the CBF convolution module was constructed using the FReLU activation function to enhance the network's spatial resolution capability and optimize detection accuracy. Secondly, a coordinate attention mechanism was embedded into the neck part of the network to enhance its feature fusion capability, enabling the extraction of more rich feature information. Finally, the SIoU loss replaces the YOLOv5s loss function to improve the regression accuracy of the predicted box. Through ablation experiments and visualization comparisons on the NEU-DET dataset, it is demonstrated that the mAP value of the YOLOv5s-FCS network reaches 0.747, representing an improvement of 8.3% compared to the original YOLOv5s network, 11.8% compared to the YOLOv3 network, 4.2% compared to the YOLOXs network, and 1.4% compared to the YOLOv6s network, thus demonstrating the feasibility and effectiveness of the proposed method.