Steel is the pillar industry of China, and its surface quality is the key to affecting the performance and price of steel. In order to solve the problems of poor accuracy, low efficiency and high model complexity in strip surface defect detection, a lightweight strip surface defect detection model (PGS-YOLO) was proposed and improved. Firstly, a more flexible PReLU activation function was introduced, and the slope of the negative region of the input data was adaptively adjusted through the learnable parameters, so as to improve the accuracy of the model to locate defects. Secondly, the Re-VGG is integrated into C3 to build a lightweight and efficient Re-C3 module to reduce the complexity of the model and improve the computational efficiency. Finally, the lightweight SCDown downsampling operation is adopted to reduce redundant calculations and improve the richness of feature fusion. Experimental results on the NEU-DET dataset show that the mAP of the model is increased by 6.7% to 79.9% compared with the benchmark model. The number of parameters and the amount of computation are reduced by 29.7% and 27.2%, respectively, and the FPS is increased by 2.7%, which better balances the relationship between detection accuracy, inference speed and lightweight. In addition, the model shows good generalization ability on both the WF10-DET dataset and the PCB_DATASET dataset, which meets the needs of actual engineering deployment and is expected to have important promotion and application value in engineering applications.