ArticleSensors (Basel, Switzerland)2023
WD-YOLO: A More Accurate YOLO for Defect Detection in Weld X-ray Images.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- YOLO11-Based Weld Defect Detection Method for X-Ray Images Integrating SIoU Bounding Box Regression and P2 Shallow Feature Enhancement.Sensors (Basel, Switzerland) · 2026Article
- Design of lightweight metal surface defect detection technology for YOLOv7-tiny using Anchor-Free algorithm.Scientific reports · 2026Article
- Design of multi-mode intelligent system architecture for surface defect detection of steel based on cloud technology.Scientific reports · 2025Article
- CM-YOLO: A Multimodal PCB Defect Detection Method Based on Cross-Modal Feature Fusion.Sensors (Basel, Switzerland) · 2025Article
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3 authors.
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Abstract
X-ray images are an important industrial non-destructive testing method. However, the contrast of some weld seam images is low, and the shapes and sizes of defects vary greatly, which makes it very difficult to detect defects in weld seams. In this paper, we propose a gray value curve enhancement (GCE) module and a model specifically designed for weld defect detection, namely WD-YOLO. The GCE module can improve image contrast to make detection easier. WD-YOLO adopts feature pyramid and path aggregation designs. In particular, we propose the NeXt backbone for extraction and fusion of image features. In the YOLO head, we added a dual attention mechanism to enable the model to better distinguish between foreground and background areas. Experimental results show that our model achieves a satisfactory balance between performance and accuracy. Our model achieved 92.6% mAP@0.5 with 98 frames per second.
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