To address the problems of few shots and varying sizes in the surface defects on steel strips in industrial scenarios, this study proposes a detection network for surface defects on steel strips readily applicable to few-shot situations. Specifically, the algorithm is based on the you only look once version 5 small (YOLOv5s) framework and a multi-scale path aggregation network with an attention mechanism is designed to serve as the neck of the model and thereby enhance the ability of the model to predict the defect objects on multiple scales. Then, a self-adaptive decoupled detection structure is proposed to alleviate the contradiction among classification and positioning tasks in few-shot scenarios. Finally, a bounding box regression loss function fused with the Wasserstein distance is presented to improve the accuracy of the model in detecting small defect objects. Experiments show that the proposed model outperforms other few-shot object detection models on the few-shot dataset of surface defects on steel strips, indicating that it is more suitable for few-shot defect detection tasks in industrial environments.