As an important part of intelligent manufacturing, pixel-level surface defect detection (SDD) aims to locate defect areas through mask prediction. Previous methods adopt the image-independent static convolution to indiscriminately classify per-pixel features for mask prediction, which leads to suboptimal results for some challenging scenes such as weak defects and cluttered backgrounds. In this paper, inspired by query-based methods, we propose a Wavelet and Prototype Augmented Query-based Transformer (WPFormer) for surface defect detection. Specifically, a set of dynamic queries for mask prediction is updated through the dual-domain transformer decoder. Firstly, a Wavelet-enhanced Cross-Attention (WCA) is proposed, which aggregates meaningful high- and low-frequency information of image features in the wavelet domain to refine queries. WCA enhances the representation of high-frequency components by capturing relationships between different frequency components, enabling queries to focus more on defect details. Secondly, a Prototype-guided Cross-Attention (PCA) is proposed to refine queries through meta-prototypes in the spatial domain. The prototypes aggregate semantically meaningful tokens from image features, facilitating queries to aggregate crucial defect information under the cluttered backgrounds. Extensive experiments on three defect detection datasets (i.e., ESDIs-SOD, CrackSeg9k, and ZJU-Leaper) demonstrate that the proposed method achieves state-of-the-art performance in defect detection.