.png){kind=icon}
.png){kind=icon}
Return
Weld defect detection based on improved YOLOv8n
.png){kind=icon}
PRE
AI
DOI：10.1177/08953996261433937.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Background Industrial weld defect detection is challenged by the minimal grayscale contrast between defects and the background, as well as by blurred defect edges, which together hinder the performance of detection algorithms. Moreover, practical industrial environments require high detection accuracy, fast inference speed, and flexible deployment.Objective To address these challenges, this study proposes an improved YOLOv8n defect detection method that enables more accurate, faster, and lightweight automated weld defect detection.Methods The key improvements are as follows. First, in the backbone, the original C2f module is replaced by the C2f_OREPA feature extraction module, constructed with the Online Convolution Parameterization Approach (OREPA), which reduces computational complexity and enhances feature representation. Second, a downsampling module, DCDConv, is introduced to replace the conventional convolution after the first standard convolution layer, allowing better preservation of fine defect features and improving the detection of subtle defects. Additionally, in the neck, a cross-scale feature fusion module (CCFM) is incorporated to improve detection performance across defects of different scales.Results Experiments on our self-constructed dataset comprising eight weld defect categories show that the improved model achieves a mean average precision (mAP) of 87.6%, a 4.5% increase over the original YOLOv8n. Meanwhile, the model reduces the number of parameters by 26.9%, decreases computational cost by 35.7%, and achieves an inference speed of 103 frames per second (FPS). On the public NEU-DET dataset, the improved model obtains an mAP of 82.8%, outperforming the original YOLOv8n by 6.7%. Overall, the proposed model surpasses mainstream object detection frameworks, including YOLOv8n, YOLOv12n, Faster R-CNN, and RetinaNet.Conclusion In summary, the proposed method provides an accurate, efficient, and deployment-friendly solution for weld defect detection in industrial applications, demonstrating substantial practical value.
Keywords:
weld defect detection
YOLOv8n
lightweight
deep learning
DCDConv
C2f_OREPA
Journal
J
IF:
1.4
Papers:
37
Citations:
1.1K