Given that small targets are predominant in the steel surface defect areas, most existing methods cannot balance the trade-off between detection accuracy and speed. In this paper, we propose a steel surface defect detection algorithm based on YOLOv7-tiny (GBS-YOLOv7t). Firstly, we design the GAC-FPN network to fully integrate the target semantic information progressively and across layers, aiming to address the limited information flow issue in traditional feature pyramids. Secondly, we embed a bi-level routing attention (BRA) module to endow the model with dynamic query and sparse perception capabilities, thus enhancing the detection accuracy of small targets. Thirdly, we introduce the SIoU loss function to improve the training and inference capabilities of the model, and to enhance the network robustness. Experimental validation on the public dataset NEU-DET demonstrates an mAP of 72.9% and a precision of 69.9% for GBS-YOLOv7t, achieving improvements of 4.2% and 8.5%, respectively, over the original YOLOv7-tiny model. The FPS reaches 104.1 frames, indicating strong real-time performance. Compared to other detection algorithms, GBS-YOLOv7t is more effective in detecting small targets in steel surface areas, with experimental results showing that the improved algorithm better balances the detection accuracy and speed.