## 2 Related Work  
### Yolo  
In the field of object detection algorithms, the YOLO series [9; 10] has become extremely influential due to its end-to-end architecture and fast inference speed. From YOLOv3's multi-scale prediction and residual networks [11], through YOLOv5's in-depth model and training optimization [12], to YOLOv7 [13] and YOLOv8's [14; 15] lightweight design and attention mechanisms integration, this series has continuously improved detection accuracy and efficiency.  
However, when applied to industrial defect detection, the YOLO series faces challenges in modeling complex deformations. Confronted with scenarios such as complex backgrounds, small objects, and non-rigid targets, researchers have integrated advanced modules like DCN [16], multi-scale attention mechanisms [17], and lightweight convolution units (e.g., GhostConv [18], ShuffleNet [19]) into the YOLO framework to enhance model perception. However, these improvements are mostly simple combinations of single modules, lacking systematic fusion to comprehensively boost industrial defect identification.  
A significant recent trend, particularly pronounced in 2024-2025, has been the emergence of highly efficient YOLO variants explicitly optimized for industrial defect detection. Models like FMV-YOLO [20], WSS-YOLO [21], etc., represent the latest state-of-the-art advancements.  
These models share the following core design objectives with our work: enhancing performance on challenging industrial defects under tight computational constraints. They commonly employ techniques such as multi-scale feature fusion, deformable convolutions, various attention modules (e.g., channel, spatial, hybrid), and aggressive lightweight strategies, and have been extensively evaluated on standard industrial defect benchmarks like NEU-DET and GC10-DET. They each offer unique innovations; FMV-YOLO utilizes feature map variation modules, and WSS-YOLO explores weighted spatial sampling.  
However, these approaches often optimize either speed or accuracy aggressively, sometimes incurring trade-offs (e.g., quantization loss, limited generalization to diverse defects) or introducing substantial complexity. Furthermore, there remains potential to achieve a more synergistic integration of multi-scale attention and adaptive spatial features specifically tailored for the geometric and textural variability inherent in steel defects.  
As an influential iteration, YOLOv8 incorporates key enhancements, as follows: (1) an evolved CSPDarknet backbone for richer feature extraction; (2) a novel anchor-free head (Detect) for simplified structure; (3) advanced attention like the context aggregation module (CA); (4) flexible model scaling for diverse deployments. Its speed-accuracy balance makes it a strong base for complex industrial inspection.  
Consequently, building upon the foundations laid by YOLOv8 and recent lightweight advancements, this paper proposes YOLO-MAD, a YOLO-based multi-scale attention and deformable convolution fusion method. It enhances the response to defect salient regions with integrated multi-scale and multi-channel attention mechanisms, and improves spatial adaptability to deformed defects using strategically placed deformable convolution modules. This synergistic fusion of multi-scale attention and deformable convolution (MAD) aims to significantly boost robustness and accuracy in complex industrial inspection scenarios without compromising efficiency.