标题： YOLO-FDA：融合分层注意力和细节增强的表面缺陷检测 显示英文标题

标题： YOLO-FDA: Integrating Hierarchical Attention and Detail Enhancement for Surface Defect Detection

摘要： 工业场景中的表面缺陷检测由于缺陷类型广泛、形状和尺寸不规则、细粒度要求高以及材料纹理复杂，既重要又具有技术挑战性。 尽管基于人工智能的检测器最近取得了进展，提高了性能，但现有方法通常存在冗余特征、细节敏感性有限以及在多尺度条件下的鲁棒性较弱的问题。 为了解决这些挑战，我们提出了YOLO-FDA，这是一种新颖的基于YOLO的检测框架，集成了细粒度细节增强和注意力引导的特征融合。 具体来说，我们采用类似BiFPN的架构，以加强YOLOv5主干网络内的双向多级特征聚合。 为了更好地捕捉细微的结构变化，我们引入了一个细节方向融合模块（DDFM），在第二低层引入方向性非对称卷积，以丰富空间细节，并将第二低层与低层特征融合，以增强语义一致性。 此外，我们提出了两种新的基于注意力的融合策略，即注意力加权拼接（AC）和跨层注意力融合（CAF），以提高上下文表示并减少特征噪声。 在基准数据集上的大量实验表明，YOLO-FDA在各种类型的缺陷和尺度上，无论是在准确性还是鲁棒性方面，都能持续优于现有的最先进方法。 显示英文摘要

摘要： Surface defect detection in industrial scenarios is both crucial and technically demanding due to the wide variability in defect types, irregular shapes and sizes, fine-grained requirements, and complex material textures. Although recent advances in AI-based detectors have improved performance, existing methods often suffer from redundant features, limited detail sensitivity, and weak robustness under multiscale conditions. To address these challenges, we propose YOLO-FDA, a novel YOLO-based detection framework that integrates fine-grained detail enhancement and attention-guided feature fusion. Specifically, we adopt a BiFPN-style architecture to strengthen bidirectional multilevel feature aggregation within the YOLOv5 backbone. To better capture fine structural changes, we introduce a Detail-directional Fusion Module (DDFM) that introduces a directional asymmetric convolution in the second-lowest layer to enrich spatial details and fuses the second-lowest layer with low-level features to enhance semantic consistency. Furthermore, we propose two novel attention-based fusion strategies, Attention-weighted Concatenation (AC) and Cross-layer Attention Fusion (CAF) to improve contextual representation and reduce feature noise. Extensive experiments on benchmark datasets demonstrate that YOLO-FDA consistently outperforms existing state-of-the-art methods in terms of both accuracy and robustness across diverse types of defects and scales.