Accurate detection of surface defects is crucial for maintaining the quality of strip steel products. We propose a lightweight strip steel surface defect detection model, YOLOv8-VRLG, to address the issues of insufficient detection accuracy and large parameter sizes in existing models. First, the VanillaNet module was integrated into YOLOv8 to preserve efficient feature extraction capabilities while effectively lightening the backbone network. Second, the C2fRL module, which integrates the benefits of RepGhost and large selective kernel modules into the original C2f, was introduced to further reduce the number of parameters and dynamically adjust processing details based on image content, thereby enhancing detection accuracy. Finally, the traditional convolutional layers in the detection head were replaced with the GSConv module. This module optimizes information exchange and feature fusion with its unique mixed convolution strategy, thereby improving the model’s accuracy and computational resource efficiency. Extensive tests on the NEU-DET dataset demonstrated that the mean average precision of the YOLOv8-VRLG model improved by 1.8% compared with that of the YOLOv8n model. In addition, the computational cost and parameter count were reduced by 23.5% and 28.6%, respectively. Tests on the GC10-DET dataset further showcased the model’s exceptional adaptability and robustness in complex environments.