StripePoint-YOLO: Task-Adaptive Detection of Multi-Type Weld Seam Keypoints in Noisy Industrial Welding Scenes

To address the difficulty in stably detecting weld seam keypoints under complex industrial interferences, such as intense arc light, spatter, reflection, and partial occlusion, this paper proposes a lightweight weld seam keypoint detection model named StripePoint-YOLO. The proposed method formulates five typical types of weld seams as a unified detection-based keypoint localization task. Built upon YOLO11n, the model introduces a P2 detection head to enhance shallow high-resolution feature representation for keypoints and adopts SPDConv to reduce the loss of local details caused by early-stage downsampling. Meanwhile, the P5 detection output layer is removed, while its deep semantic features are retained for top-down feature fusion. This design reduces the negative influence of redundant coarse-scale predictions on the center localization of tiny keypoints. For optimization and training, WIoU v3 and NWDLoss are adopted as a joint regression loss to improve the stability of small-scale keypoint bounding box regression. In addition, an online physics-driven data augmentation strategy, OPDDA, is designed to simulate welding disturbances such as arc light, spatter, and dynamic occlusion. Experimental results show that StripePoint-YOLO achieves an mAP@50-95 of 80.46%, a Mean Center Error (MCE) of only 2.33 px, a parameter count of 1.86 M, and a computational cost of 19.42 GFLOPs, while reaching an inference speed of 159.80 FPS under the reported hardware configuration. Further MCE visualization and localization error analysis demonstrate that the proposed method maintains stable keypoint center localization across multiple weld seam types and complex noisy scenarios, verifying the effectiveness of StripePoint-YOLO for accurate and efficient weld seam keypoint detection in industrial welding images.