<p>Current models for detecting defects on steel surfaces struggle to fully utilize potential positional and semantic information. Usually, these models merely combine high-level and low-level features in a straightforward manner, leading to an increase in redundant information. To address this challenge, this study presents an aggregated multi-level feature interaction fusion network (AMFNet). Specifically, the AMFNet incorporates a branch interaction module (BIM) that branches and fuses features channel-wise to facilitate feature interaction. Moreover, it also includes a dilated context module (DCM) that expands the receptive field to capture contextual information across various scales effectively. In addition, we propose a spatial correlation module (SCM) that is designed to recognize spatial dependencies between adjacent feature maps and generate attention weights. Our performance evaluations on the NEU-DET and GC10-DET dataset reveal that our proposed AMFNet significantly outperforms classical object detectors in terms of mean average precision (mAP). Moreover, it also demonstrates a modest improvement over the advanced methods recently introduced by other researchers.</p>