为改善因小目标检测效果差而导致的钢铁表面缺陷检测精度差的问题，以YOLOv5s为基础，通过在主干网络添加注意力机制（attention mechanism, SE），将C2f模块代替C3模块，将双向特征金字塔网络（bidirectional feature pyramid network, BIFPN）网络代替颈部网络中的路径聚合网络（path aggregation network, PAN）网络的这三种方法来提升模型对缺陷小目标的检测能力。旨在提升检测精度并达到实时检测要求。结果表明，改进后的YOLOv5s-SCB算法在NEU-DET（northeastern university-detect）上的均值平均精度（mean average precision, mAP）值达到77.9%，在达到实时检测的前提下，相较于YOLOv5s网络提高了3.7%，与其余基于YOLOv5s改进的算法及YOLOv8相比，YOLOv5s-SCB实现了更好的检测效果。可见本文提出的钢铁表面缺陷检测算法YOLOv5S-SCB可以更好地完成钢铁表面缺陷检测。

In order to improve the poor accuracy of steel surface defects caused by poor detection of small targets, on the basis of the YOLOv5s, by adding the SE (attention mechanism) in the backbone network mechanism, C2f module instead of C3 module, the BIFPN (bidirectional feature pyramid network) instead of the PAN (path aggregation network) network in the neck, these three methods were used to investigate the improvement of the ability to the defect of small target detection. It aims to improve the detection accuracy and achieve real-time detection. The results show that the mAP (mean average precision) of the improved YOLOv5s-SCB algorithm on NEU-DET (northeastern university-detect) reaches 77.9%, which is 3.7% higher than that of the YOLOv5s network on the premise of real-time detection. Compared with other improved algorithms based on YOLOv5s and YOLOv8, YOLOv5S-SCB achieves better detection effect. It is concluded that the proposed steel surface defect detection algorithm YOLOv5S-SCB can better detect defects on steel surfaces.