What question did this study set out to answer?

The research aims to improve the accuracy and speed of weld defect detection methods in industrial settings.

March 26, 2026

Weld defect detection based on improved YOLOv8n

Key Points

The research aims to improve the accuracy and speed of weld defect detection methods in industrial settings.
Proposed an improved YOLOv8n architecture for defect detection.
Replaced the original C2f module with a C2f_OREPA feature extraction module for better feature representation.
Introduced a DCDConv downsampling module to preserve fine defect features.
Incorporated a cross-scale feature fusion module to enhance detection across varying defect scales.
Achieved a mean average precision (mAP) of 87.6%, a 4.5% improvement over the original YOLOv8n.
Reduced parameters by 26.9% and computational cost by 35.7%.
Attained an inference speed of 103 frames per second (FPS) on a self-constructed dataset.
On the public NEU-DET dataset, reached an mAP of 82.8%, outperforming YOLOv8n by 6.7%.
Surpassed mainstream object detection frameworks like YOLOv12n, Faster R-CNN, and RetinaNet.

Abstract

BackgroundIndustrial 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. ObjectiveTo address these challenges, this study proposes an improved YOLOv8n defect detection method that enables more accurate, faster, and lightweight automated weld defect detection. MethodsThe key improvements are as follows. First, in the backbone, the original C2f module is replaced by the C2fOREPA 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. ResultsExperiments 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. ConclusionIn summary, the proposed method provides an accurate, efficient, and deployment-friendly solution for weld defect detection in industrial applications, demonstrating substantial practical value.

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