The rise of large models, often referred to as foundational models, has led to considerable progress in the field of artificial intelligence research. Our empirical findings indicate that the large models might struggle or deliver poor performance when it comes to specific surface segmentation challenges, including the identification and segmentation of defects on strip steel surfaces (S³D) and the detection of imperfections on magnetic tile surfaces. To apply the large model to defects segmentation, rather than fine-tuning the large model, we propose Segmentation-Driven Image Enhancement (SDIE), using several classic filters to enhance the input images. In this case, the weights of the filters in multiple layers are controlled by reinforcement learning. Then, we test our method on two S³D datasets with different few-shot settings. Our method accomplishes the task brilliantly compared with other methods for S³D such as CPANet. We believe that our work not only opens up opportunities for downstream tasks such as segmenting industrial defects using large models, but may also have potential applications in various fields in the future, including medical image processing, remote sensing image analysis, agriculture and more.