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DEIM-SFA: A Multi-Module Enhanced Model for Accurate Detection of Weld Surface Defects.

Yan Sun¹, Yingjie Xie¹, Ran Peng¹

¹College of Information Engineering, Sichuan Agricultural University, Ya'an 625000, China.

Sensors (Basel, Switzerland)

|October 29, 2025

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This study introduces DEIM-SFA, a new framework for automated welding defect detection. It significantly improves accuracy by better retaining fine-grained features and fusing multi-scale information, enhancing structural safety in manufacturing.

Area of Science:

Manufacturing
Materials Science
Computer Vision

Background:

Automated detection of metal welding defects is crucial for manufacturing.
Existing methods lack fine-grained feature retention and efficient multi-scale fusion, leading to low accuracy.
Complex backgrounds often interfere with defect detection systems.

Purpose of the Study:

To develop a novel detection framework, DEIM-SFA, for high-precision automated visual inspection of welding defects.
To address limitations in fine-grained feature retention, multi-scale information fusion, and background interference.

Main Methods:

Introduced structure-aware dynamic convolution (SPD-Conv) to focus on defect structures and suppress noise.
Designed a multi-scale dynamic fusion pyramid (FTPN) for efficient aggregation of multi-scale features.

Keywords:

DEIM feature fusion industrial vision welding defect detection

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Integrated a lightweight multi-scale attention module (EMA) to enhance salient region localization.

Main Results:

DEIM-SFA achieved significant improvements: 3.9% in mAP50, 4.3% in mAP75, 3.7% in mAP50-95, and 1.4% in Recall.
Demonstrated superior detection accuracy across various target sizes.
Maintained balanced model complexity and efficient inference compared to SOTA methods.

Conclusions:

The DEIM-SFA framework offers a robust solution for automated welding defect detection.
The proposed methods effectively enhance feature extraction and fusion for improved accuracy.
DEIM-SFA surpasses existing methods in detecting welding defects in industrial machine vision.