A high precision and lightweight method for steel surface defect detection based on improved YOLOv5

Detecting surface defects in steel is essential for ensuring structural safety and manufacturing efficiency. However, existing detection systems often struggle to accurately identify small flaws, handle complex surface conditions, and maintain real-time performance. To overcome these challenges, this study proposes ASFRW-YOLO, an enhanced version of the YOLOv5 framework. The model integrates a multi-scale ASF module to improve sensitivity to minute defects, replaces the conventional C3 module with RepNCSPELAN4 to strengthen feature representation, and adopts the Wise-IoU loss with adaptive weighting to refine bounding box regression. Experiments were conducted on the NEU-DET dataset, which was divided into training, validation, and testing sets with an 8:1:1 ratio. The proposed method achieved a mean Average Precision of 83.2% at an IoU threshold of 0.5 and 46.4% across IoU thresholds from 0.5 to 0.95, representing an improvement of approximately seven percentage points compared with YOLOv5s. Moreover, the model maintains a lightweight design with only 6.20 million parameters and processes 640 × 640 input images at about 125 frames per second on an RTX 4060 Laptop GPU (8 GB VRAM). These results demonstrate that ASFRW-YOLO effectively balances detection accuracy, computational efficiency, and model compactness, making it highly suitable for real-time industrial defect inspection.