In response to the problems that current deep learning methods often fail to achieve sufficient accuracy in detecting multi-scale features and have poor performance in identifying minor defects when detecting surface defects of hot-rolled steel strips, a lightweight defect detection algorithm for hot-rolled steel strips, HGFD-YOLOv8, was proposed. The innovation of this algorithm lies in: integrating HGnetv2 and lightweight convolutional LightConv to reconstruct the feature extraction network, reducing the number of parameters while optimizing the feature extraction ability; designing a gradient-weighted feature fusion pyramid network (GWFFPN), leveraging the progressive fusion and dynamic weighting strategies to effectively alleviate the semantic loss problem in the process of feature interaction between non-adjacent layers; constructing a dynamic multi-scale feature extraction module (DMF), embedding the simultaneous attention mechanism (SimAM) to achieve dynamic allocation of feature channels, precisely focusing on the core area of defects, and significantly improving the performance of identifying minor defects. Experimental results show that compared with the baseline model YOLOv8n, the HGFD-YOLOv8 algorithm reduces the number of parameters by 31.2% on the NEU-DET dataset for hot-rolled steel strip surface defects, increases the mean accuracy by 5.1%, and improves the precision and recall rates by 8.6% and 5.4% respectively. Compared with detection algorithms such as YOLOv13 and DEIM, the proposed defect detection algorithm has better detection performance and industrial application value.