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WMC-DFINE: An Improved DFINE Model for Aluminum Profile Surface Defect Detection.

Pengfei He^1,2, Yunming Ding¹, Shuwen Yan¹

¹School of Physics and Electronic Information, Yantai University, Yantai 264005, China.

Sensors (Basel, Switzerland)

|May 27, 2026

Summary

This summary is machine-generated.

This study introduces WMC-DFINE, an advanced algorithm for detecting aluminum profile surface defects. It significantly improves accuracy and efficiency in industrial quality control by suppressing background noise and enhancing defect detection.

Keywords:

DFINE aluminum profile surface defect detection cross-scale feature fusion knowledge distillation wavelet transform

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Area of Science:

Materials Science
Computer Vision
Industrial Engineering

Background:

Automated inspection of aluminum profiles is crucial for industrial quality control.
Existing machine vision methods struggle with background texture interference and detecting defects with extreme aspect ratios.

Purpose of the Study:

To develop an end-to-end defect detection algorithm for aluminum profiles that overcomes current limitations.
To enhance the accuracy and efficiency of automated surface defect inspection.

Main Methods:

Introduced the Wavelet-Integrated Frequency Attention (WIFA) module for noise suppression and edge enhancement.
Designed a Cross-Scale Feature Fusion (CSFF) module to maintain defect feature continuity.
Incorporated a Multi-Kernel Strip Shuffle (MKSS) module to capture slender scratch features.
Employed knowledge distillation to create a lightweight, efficient student model.

Main Results:

WMC-DFINE achieved a mean Average Precision (mAP) of 82.1% on the Tianchi aluminum defect dataset, outperforming YOLOv12 and RT-DETR.
The distilled WMC-DFINE-distill model showed a 3.2% mAP improvement over the baseline DFINE.
The distilled model reduced parameters by 47% and achieved 59.75 FPS inference speed.

Conclusions:

The WMC-DFINE algorithm effectively balances background suppression and defect detail preservation.
This method offers a practical and efficient solution for real-time industrial defect inspection of aluminum profiles.
The knowledge distillation strategy yields a highly efficient model for practical deployment.