## 2 Related Work
### Industrial Defect Detection  
Industrial surface defect detection serves as a critical component in manufacturing quality assurance, significantly enhancing product reliability and safety. Current methodologies can be broadly classified into the following two paradigms: conventional machine vision systems and modern deep learning solutions. Traditional machine vision approaches extract defect features via threshold segmentation, edge detection, etc., but have limited performance for complex industrial surface defects and are sensitive to lighting and noise. As deep learning technology developed, CNN-based detection methods became mainstream. Examples include the 2016 R-CNN by [PERSON] et al. [22], the 2016 YOLOv1 by [PERSON] et al. [9], and the 2017 Mask R-CNN by [PERSON] et al. [23] However, these methods still fall short when dealing with tiny or irregular defects. Subsequent innovations have introduced hybrid solutions to address these constraints, such as [PERSON] et al. 's GAN-based data augmentation [24] for enhanced generalization, followed by breakthroughs in self-supervised learning [25] and multi-modal feature fusion methods [26]. In particular, for steel surface defect detection, research has actively explored integrating state-of-the-art architectural components into detectors. AKConv (arbitrary kernel convolution) [27], with its ability to dynamically learn optimal kernel shapes and offsets, has been integrated into various detectors to better model the diverse morphologies of steel defects like scratches, inclusions, and patches, showing promise in handling irregular deformations. The BiFPN (bidirectional feature pyramid network) [28] is widely adopted to enhance multi-scale feature fusion, proving particularly effective in improving the detection of small and low-contrast defects prevalent in steel imagery. Similarly, mechanisms inspired by Dynamic Head [29], focusing on learning scale-aware, spatial-aware, and task-aware attention in detection heads, have been successfully applied to boost the sensitivity and discrimination capability for intricate and ambiguous defect features on steel surfaces. These technologies represent leading-edge solutions addressing core challenges in the domain. Nevertheless, existing deep-learning-based methods still face challenges in handling tiny/irregular defects and class-imbalance issues. Improving detection models' real-time performance and robustness for complex industrial environments remains an urgent problem. In light of these challenges, we conducted a thorough review of several notable enhancements to YOLO-based models. For instance, [PERSON] et al. assembled the CBS, MS, and SPPF modules to implement multi-scale block and spatial attention to improve YOLOv5 [30]. [PERSON] et al. enhanced YOLOv8 by introducing channel-wise attention, spatial-wise attention, and full 3D weights for attention [31]. Additionally, [PERSON] et al. implemented a multi-scale feature fusion (MSF) module and an attention mechanism residual block (CRA block) to optimize the traditional YOLOv5 [32]. These approaches have significantly influenced the development of our \"Multi-Scale Geometric Structure Feature Extraction and Fusion\" concept. Critically, while the aforementioned advanced modules (AKConv, BiFPN, dynamic head) have demonstrated efficacy in isolation or pairwise combinations within previous defect detection frameworks, a comprehensive and synergistic integration targeting all aspects of feature representation (feature extraction, multi-scale fusion, and discriminative prediction head) specifically for steel defects remains largely unexplored. Their combined potential for maximizing detection performance is yet to be fully harnessed. To overcome these shortcomings, this paper introduces a novel metal surface defect detection framework that integrates three advanced components, as follows: the BiFPN feature fusion strategy from EfficientDet, the superior AKConv dynamic variable convolution kernel, and the Dynamic Head 3D attention mechanism.