SA-DETR: Lightweight Structure-Aware Defect Detection Model for Sustainable Low-Carbon Steel Manufacturing Based on RT-DETR-L

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Steel manufacturing is a typical high-energy and high-carbon industry. Undetected tiny surface defects such as scratches and crazing lead to massive waste of steel raw materials and extra energy consumption in subsequent processing, while heavy detection models may increase the computational energy demand of on-site inspection systems. The purpose of this study is to develop a lightweight defect detection model that improves the recognition of weak-semantic and structurally distinctive surface defects while reducing computational cost for industrial deployment. To achieve this objective, we propose SA-DETR, a lightweight structure-aware defect detection model built on RT-DETR-L. Three targeted modules are introduced: O-EHGBlock retains fine-grained structural cues of micro-defects with reduced computation; Adaptive Structure-aware Query Selection (ASQS) supplements semantic query ranking with local structural information to improve the detection of weak-semantic defects; and lightweight StarC3 replaces redundant fusion blocks to cut computational overhead. Evaluated on NEU-DET and PCB datasets, SA-DETR boosts mAP@0.5 by 4.5 percentage points over RT-DETR-L, while reducing parameters by 36.0% and GFLOPs by 46.5%, with inference speed lifted to 103.0 FPS. These results demonstrate that SA-DETR provides a favorable trade-off between detection accuracy and computational efficiency, indicating its potential for resource-constrained industrial surface inspection and sustainable steel quality control.