## 1 Introduction  
With the accelerating pace of industrialization and the continuous advancement of infrastructure construction, industrial materials such as steel and aluminum are increasingly being applied in fields including aerospace, energy, and automotive manufacturing. Ensuring product reliability and safety has made surface defect detection a critical focus of quality management [1]. Traditional defect detection methods rely heavily on human expertise, image processing algorithms, or hand-crafted feature extractors [2, 3]. Although these approaches can yield acceptable results for regular defects under controlled conditions, they generally suffer from inconsistent detection accuracy, particularly for subtle or complex defect morphologies (e.g., micro-cracks, non-uniform corrosion patterns) [4], and limited adaptability to complex industrial backgrounds characterized by noise, texture variations, or overlapping structures [5]. Recent advancements in deep learning-based object detection [6] have rapidly evolved and demonstrated significant advantages in industrial defect inspection due to their powerful feature extraction and pattern recognition capabilities. Among these, the YOLOarchitecture [7] has gained prominence in industrial applications due to its real-time processing and end-to-end design. However, conventional YOLO models still face several challenges when dealing with surface defects in real-world steel inspection scenarios, such as difficulties in recognizing structurally irregular or slender defects, significant reductions in detection accuracy, relatively static feature fusion mechanisms that fail to capture salient differences across multi-scale defects, and shallow shared convolutional structures in the original detection head that lack task awareness, leading to mutual interference between classification and localization [8]. To overcome the constraints of YOLOv8n in steel surface defect detection, particularly its limited perceptual scope, suboptimal integration of multi-level features, and coupled classification-regression operations, this paper presents YOLO-MAD, an enhanced lightweight framework. This approach adopts a modular design, maintaining the architectural advantages of the YOLOv8 backbone while introducing three key improvements:  
* **AKConv (adaptive kernel convolution)** is integrated into the backbone to replace standard convolution modules. By introducing dynamic sampling locations and a learnable mask mechanism, AKConv enables the convolutional kernels to adaptively focus on fine-grained and irregular defects (e.g., micro-cracks), substantially boosting the network's sensitivity to minute structural discontinuities. * **BiFPN (bidirectional feature pyramid network)** is adopted to replace the original PANet in the neck. By incorporating a learnable weighted feature fusion strategy, BiFPN strengthens the information flow across multi-scale features and facilitates effective interaction among features at different levels. This design improves the model's expressive power in handling defects of varying scales. * **Detect_DyHead (Detect_DynamicHead)** is employed in the detection head, which integrates novel task-specific, spatial-contextual, and scale-sensitive attention layers on top of the existing branch separation. This integration further enhances the response to critical regions and improves robustness and accuracy in complex textured backgrounds beyond the baseline separation. This paper is structured as follows: Section 2 reviews related work on the YOLO architecture and industrial defect detection. Section 3 presents the overall framework of YOLO-MAD and elaborates on the design principles and implementation details of each module. Section 4 provides performance comparisons on the NEU-DET and GC10-DET datasets, supplemented by ablation studies and visual analyses. Section 5 critically examines the empirical findings from Section 4 through rigorous quantitative and qualitative assessments.