For limited equipment resources in industrial scenarios, a lightweight strip steel defect detection model based on improved YOLOv5 is proposed. First, ShuffleNetv2 is used to replace the backbone feature extraction network to optimize model parameter amount and running speed; secondly, the lightweight up-sampling operator, namely content-aware reassembly of features (CARAFE) is used to further reduce parameters and calculation amount while increasing the receptive field. At the same time, the GSConv layer is introduced to balance the model accuracy and detection speed while ensuring semantic information. Finally, a cross-level feature fusion mechanism is designed to improve the detection accuracy of the network. The experimental results show that the mean average precision of the improved model is 78.5%, which is 1.4% higher than the original YOLOv5 algorithm. The calculation amount of the model is 10.9 GFLOPs; the parameter amount is 5.88×10⁶; the calculation and parameter amounts are reduced by 31% and 15.4%, respectively; the detection speed is 49 f/s, which is increased by 3.5 f/s. Therefore, the improved model improves the detection accuracy and speed and greatly reduces the calculation and parameter amounts of the model, which can ensure the real-time detection of surface defects of strip steel.