In response to the deficiencies of existing steel surface defect detection algorithms in terms of resource consumption, detection accuracy, and efficiency, a lightweight steel defect detection algorithm based on YOLOv8n (FCM-YOLOv8n) is proposed. First, a frequency-aware feature fusion network is utilized to efficiently extract and integrate high-frequency information, reducing computational costs while enhancing detection speed. Second, a lightweight feature interaction module (Cc-C2f) is restructured to effectively preserve spatial and channel dependencies while reducing feature redundancy, thereby lowering model parameters and computational complexity. Finally, a multi-spectrum attention mechanism is applied to mitigate feature information loss in the frequency domain, improving the accuracy of detecting complex defects. Experimental results on the Severstal and NEU-DET steel defect datasets show that, compared to YOLOv8n, the FCM-YOLOv8n algorithm achieves a 2.2% and 1.5% improvement in mAP@0.5, respectively, with a 0.5 M and 1.5 G reduction in parameters and computational complexity. The FPS reaches 143 f/s and 154 f/s, respectively, demonstrating excellent real-time performance. The algorithm achieves an optimal balance between detection accuracy, computational cost, and efficiency, providing robust support for edge device applications. Further validation on the GC10-DET dataset shows a 2.9% improvement in mAP@0.5 compared to the baseline model, fully demonstrating the algorithm's exceptional generalization ability.