Aiming at the problems of large number of parameters of YOLOv5s model and difficulty in deploying on embedded devices, a lightweight YOLOv5s strip surface defect detection method is designed. Firstly, part of the convolutional layer in the backbone network is replaced with RepGhost with multi-branching structure, which enhances the ability of the backbone to extract feature information, and the reasoning can be converted into a single-branch structure to ensure the detection speed. Secondly, a lightweight FPN network (GG-FPN) is proposed, in which G-Ghost is used to reduce redundant parameters in the C3 module, while GSConv uses the large convolutional kernels depth separate convolution and branching structures to ensure the improvement of accuracy and speed at the same time. Experiments show that on the NEU-DET dataset, the number of parameters of the GG-FPN model is reduced by 24.7% compared with the original FPN, and the GFLOPs are reduced by 20.6%. For the whole model, the improved algorithm mAP only loses 1.9%, the number of parameters is reduced by 37.5% compared with YOLOv5s, GFLOPs is reduced by 33.1%, and the detection speed reaches 187 frame/s, which better balances the speed and accuracy of detection.