In this study, we propose a reparameterization You-Only-Look-Once v5 (YOLOv5) algorithm model for strip-steel surface defect detection to address low precision and poor timeliness in traditional methods. The proposed model introduces a re-parameterized VGG Light module, an enhanced bidirectional feature pyramid network feature structure, and a bounding box regression loss function fused with a normalized Gaussian-Wasserstein distance metric to improve small target defect detection accuracy. The experimental findings reveal a mean average precision (mAP) of 82.1% on the NEU-DET dataset, representing a notable improvement of 4.1% over the baseline YOLOv5s algorithm. Furthermore, the proposed algorithm model demonstrates superior detection accuracy compared with other prevalent object detection models and effectively mitigates challenges such as false detections and missed detections of small targets. Notably, it achieves an impressive detection speed of 68 FPS, affirming its efficacy in real-time applications.