Aiming at the key challenges in steel surface defect detection, such as insufficient accuracy, high missed detection rate of small-sized defects and bounding box localization deviations, this paper proposes an improved steel surface defect detection model YOLOv8-CEW （closed eyes in the wild） based on YOLOv8. The model achieves performance breakthroughs in feature extraction, feature fusion, and bounding box regression through three core improvements: The integration of the CFBlock （ConvFormer block） module, the efficient up convolution block (EUCB) and the WIoU loss function. Specifically, the introduced CFBlock module is fused with the existing C2f module in the Backbone and replaces the internal bottleneck to optimize the feature capture mechanism for the unique morphologies of steel defects (slender and winding, dense and discrete), enhancing the discriminative ability of multi-scale defect features; the EUCB module replaces traditional upsampling to adapt to the drastic scale changes of defects under different shooting distances, optimize defect feature reconstruction and improve detection integrity; and the WIoU loss function dynamically adjusts the localization penalty strategy according to defect scale, strengthening the accuracy of bounding box localization. Experimental results on the NEU-DET （Northeastern University defect detection dataset） demonstrate that compared with the baseline YOLOv8 model, the improved model achieves increases of 3.2%, 1.4%, 1.1% and 0.4% in precision, recall, mAP@0.5 and mAP@0.5:0.95, respectively. Furthermore, its detection speed meets the requirements of industrial applications, indicating promising practical prospects.