Data enhanced YOLOv8s algorithm for X-ray weld defect detection

X-ray weld images often suffer from low contrast, poor quality, and other limitations. Additionally, due to factors such as inspection equipment and cost constraints, the number of available samples for weld images is limited, which may not suffice for deep learning training requirements. To address these challenges and enable intelligent weld seam image detection, a comprehensive approach is proposed, comprising a data enhancement method and an enhanced YOLOv8s defect detection algorithm. The method unfolds as follows: firstly, 4k+ images are reorganised into weld defect images with consistent size utilising a weld defect image feature reorganisation method, which retains as much of the weld defect information. Subsequently, data enhancement of X-ray weld defect images is carried out through translation, luminance, noise, mirroring, and cropping degradation models and the improved SRGAN model. Finally, a novel data enhancement method is proposed based on the Multi-Head Self-Attention (MHSA), the detection head, and the Channel-wise Convolutional Spatial Gating (C2f_SGE) in the improved YOLOv8s model. This enhanced defect detection method, YOLOv8sDMS, compared with the YOLOv8s model, indicates improvements in mAP50 by 0.035, mAP50:95 by 0.021, Precision by 0.037, and Recall by 0.050. The proposed improved method achieves superior results in weld defect detection.