Aiming at the low detection precision of strip steel surface defects in actual scenarios， which is prone to missed detection and false detection， a YOLOv5-CFD model consisted of CSPDarknet53， Frequency channel attention Network （FcaNet） and Decoupled head was constructed to detect strip steel defects more accurately. Firstly， Fuzzy C-Means （FCM） algorithm was used to cluster anchor boxes in NEU-DET hot-rolling strip steel surface defect detection dataset published by Northeastern University to optimize the matching degree between the prior box and the ground-truth box. Secondly， in order to extract the rich detailed information of the target area， the frequency domain channel attention module FcaNet （Frequency channel attention Network） was added to the original YOLOv5 algorithm. Finally， the decoupled head was used to separate the classification and regression tasks. Experimental results on NEU-DET dataset show that with introducing a small number of parameters to the original YOLOv5 algorithm， the improved YOLOv5 algorithm has the detection precision increased by 4.2 percentage points， the detection mean Average Precision （mAP） of 85.5%； and the detection speed reaches 27.71 Frames Per Second （FPS）， which is not much different from the original YOLOv5 so that YOLOv5-CFD can meet the real-time detection requirements.