Artificial-Weld-Crack Detection Network, YOLOv6-NW, Based on Target Recognition Technology

Aiming at the problems of scarce datasets and the low identification accuracy faced in the field of weld-crack detection, this paper proposes an artificial-weld-crack preparation method based on the doping of dissimilar metal particles to augment the number of samples of weld-crack defects. Meanwhile, data augmentation methods such as random cropping, scaling and Mosaic are combined to further enhance the richness of the samples, so as to provide strong data support for the proposed weld-crack-defect detection model. Given the limitations of storage and computational resources in industrial application scenarios, this paper designs the lightweight detection network YOLOv6-NW. It is achieved by optimizing the YOLOv6-N model for width and depth compression to efficiently identify and locate weld-crack defects. The experimental results demonstrate that YOLOv6-NW significantly outperforms YOLOv5 in terms of both model detection performance and model size. Compared to YOLOv6-N, the number of model parameters in YOLOv6-NW is only 16% of that in YOLOv6-N, yet its model detection accuracy and recall rate remain on a comparable level with YOLOv6-N. Additionally, the precision of crack detection by YOLOv6-NW under low-resolution or low-illumination conditions remains above 0.9.