Accurate identification of surface defects on steel strips is critical for manufacturing quality assurance and operational reliability. Although deep learning has greatly advanced defect detection, precise recognition remains challenging due to significant background texture interference, loss of spatial details, and semantic imbalance across multiscale features. To address these challenges, we propose A2S2C-Det, a novel detector that integrates dual-path adaptive aggregation with spatial–semantic compensation to enhance feature representation for defect detection. First, we design a plug-and-play semantic refinement bottleneck (SRB) that augments backbone features through multiscale perception and a feature-screening bottleneck, enabling the model to suppress background interference while capturing subtle defect shapes. We further introduce a dual-path adaptive aggregation (DPAA) module that fuses complementary information from cross-level semantic consistency and fine-grained structural cues via two coordinated pathways, alleviating semantic imbalance across scales. Finally, we develop a spatial-semantic gated compensation (SSGC) module that adaptively supplies semantic information to low-level features while delivering spatial details to high-level features, recovering lost spatial details in high-level features. Extensive experiments on three benchmark datasets demonstrate that our A2S2C-Det achieves mAP50 of 82.0%, 73.0%, and 91.2%, and mAP of 47.1%, 36.5%, and 60.6%, respectively, comparing favorably against current state-of-the-art methods.
Sun et al. (Thu,) studied this question.