Optimized Industrial Surface Defect Detection Based on Improved YOLOv11

In industrial manufacturing, efficient surface defect detection is crucial for ensuring product quality and production safety. Traditional inspection methods are often slow, subjective, and prone to errors, while classical machine vision techniques struggle with complex backgrounds and small defects. To address these challenges, this study proposes an improved YOLOv11 model for detecting defects on hot-rolled steel strips using the NEU-DET dataset. Three key improvements are introduced in the proposed model. First, a lightweight Guided Attention Feature Module (GAFM) is incorporated to enhance multi-scale feature fusion, allowing the model to better capture and integrate semantic and spatial information across different layers, which improves its ability to detect defects of varying sizes. Second, an Aggregated Attention (AA) mechanism is employed to strengthen the representation of critical defect features while effectively suppressing irrelevant background information, particularly enhancing the detection of small, low-contrast, or complex defects. Third, Ghost Dynamic Convolution (GDC) is applied to reduce computational cost by generating low-cost ghost features and dynamically reweighting convolutional kernels, enabling faster inference without sacrificing feature quality or detection accuracy. Extensive experiments demonstrate that the proposed model achieves a mean Average Precision (mAP) of 87.2%, compared to 81.5% for the baseline, while lowering computational cost from 6.3 Giga Floating-point Operations Per Second (GFLOPs) to 5.1 GFLOPs. These results indicate that the improved YOLOv11 is both accurate and computationally efficient, making it suitable for real-time industrial surface defect detection and contributing to the development of practical, high-performance inspection systems.