INWELD—An Industrial Dataset for Object Detection and Instance Segmentation of Weld Images in Production Scenarios

Welding is one of the most common machining methods in the industrial field, and weld grinding is a key task in the industrial manufacturing process. Although several weld-image datasets exist, most provide only coarse annotations and have limited scale and diversity. To address this gap, we constructed INWELD, a comprehensive multi-category weld dataset captured under real-world production conditions, providing both single-label and multi-label annotations. The dataset covers various types of welds and is evenly divided according to production needs. The proposed multi-category annotation method can predict the weld geometry and welding method without additional calculation and is applied to object detection and instance segmentation tasks. To evaluate the applicability of this dataset, we utilized the mainstream algorithms CenterNet and YOLOv7 for object detection, as well as Mask R-CNN, Deep Snake, and YOLACT for instance segmentation. The experimental results show that in single-category annotation, the AP50 of CenterNet and YOLOv7 is close to 90%, and the AP50 of Mask R-CNN and Deep Snake is greater than 80%. In multi-category annotation, the AP50 of CenterNet and YOLOv7 is greater than 80%, and the AP50 of Deep Snake and YOLACT is nearly 70%. The INWELD dataset constructed in this paper fills the gap in industrial weld surface images, lays the theoretical foundation for the intelligent research of welds, and provides data support and research direction for the development of automatic grinding and polishing of welds.