Aiming at the problems of low detection efficiency and poor accuracy caused by the variety of steel surface defects, strong background interference and various scale changes, this paper proposes a steel surface defect detection algorithm: CBEYOLOv5. On the basis of YOLOv5 algorithm, improve the ability of feature extraction by using coordinate attention mechanism in the trunk to strengthen the focus on the target; BiFPN is used as a special extraction network, and effective feature corresponding weights are given to fully integrate features of different scales. The model loss is calculated through EIOU, so that the model can be regressed more accurately. The experimental results on the public dataset NEUDET show that the CBEYOLOv5 algorithm mAP is 755%, 38% higher than YOLOv5, and the detection speed is also higher than some common target detection algorithms.，it can detect the defects on the steel surface more accurately and quickly.