Defect detection is an indispensable process in the strip steel production process,and existing inspection methods generally have problems such as low detection accuracy and poor real-time performance.To solve the above problems,a fast defect detection method based on lightweight YOLOv3 is proposed in this paper.MobileNetv2 is used as the backbone network and output with two scales of feature maps,so that the lightweight of the network model is guaranteed;the improved attention module is fused into the feature pyramid network (FPN) and the network is combined with the spatial pyramid pooling (SPP) to improve the learning ability of the algorithm for defects;the K-means mean clustering algorithm is used to obtain a better anchor box,and the complete-intersection over union (CIoU) is used to optimize the loss function to further improve the network performance.The proposed method has a detection speed of 70.8 FPS on the strip steel defect dataset; the number of model parameters is 7.1 MB,which is only 3.02% of YOLOv3.Experiments show that the proposed method can achieve rapid detection of defects while ensuring accuracy,and has good production line deployment capabilities.