钢表面缺陷检测是工业质量管控核心环节，针对现有基于 RGB 单模态的缺陷检测模型鲁棒性不足，空间形态缺陷误检漏检错检发生率高，提出了并行双模态空间感知融合的 PMSF-YOLOv8(Parallel multi-modal spatial-awarefusion YOLOv8)算法，采用双分支异构网络分别强化 RGB 纹理和深度空间特征学习，在中期融合阶段采用双模态特征融合模块 DFFM(dual-modal feature fusion module)通过动态权重实现多尺度特征自适应融合。 使用 NUE-RSDDS-AUG数据集进行验证，结果表明，PMSF-YOLOv8 网路模型检测准确率 mAP@0.5 达 98.6%，误报率较单模态方法降低 2.1%，实现了“高精度-低误报”平衡。

Steel surface defect detection is a core aspect of industrial quality control. Given the insufficient robustness of existing RGB single-modality-based defect detection models, which often suffer from high rates of false positives, false negatives, and incorrect detections of spatial morphological defects, the parallel multi-modal spatial-aware fusion YOLOv8 (PMSF-YOLOv8) algorithm was proposed. This algorithm employs a dual-branch heterogeneous network to enhance the learning of RGB texture and depth spatial features. In the mid-fusion stage, the dual-modal feature fusion module (DFFM) was utilized to achieve adaptive fusion of multiscale features through dynamic weights. The NUE-RSDDS-AUG dataset was used for validation. The results show that the PMSF-YOLOv8 network model achieves a detection accuracy of mAP@0.5 of 98.6%, with a false alarm rate reduced by 2.1% compared with that of single-modality methods, striking a balance between "high accuracy and low false alarms".