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WTCFNet for industrial defect detection using wavelet transform and cross layer feature fusion.

Hao Chen¹, Yu-Bo Ren²

¹Queen Mary School Hainan, Beijing University of Posts and Telecommunications, Beijing, 100876, China.

Scientific Reports

|May 23, 2026

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This study introduces WTCF-Net, a new industrial surface defect detection method. It enhances defect feature representation and recognition, improving accuracy and speed for complex industrial inspection tasks.

Area of Science:

Computer Vision
Machine Learning
Industrial Automation

Background:

Industrial surface defect detection is crucial for product quality and safety.
Existing methods struggle with complex backgrounds, tiny defects, and object variations.
Accurate defect identification remains a significant challenge in manufacturing.

Purpose of the Study:

To develop an advanced industrial defect detection method addressing current limitations.
To enhance the accurate identification of defects in complex industrial environments.
To improve the detection of multi-scale defects and subtle features.

Main Methods:

Proposed Wavelet Feature Convolution (WFC) block to preserve semantic information and enhance defect features.
Introduced Interactive Residual Module (IRM) and Interactive Residual Feature extractor (IRF) for complex background defect recognition.

Keywords:

Cross-layer feature fusion Defect detection Defect regression loss Interactive residual module Wavelet convolution

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Developed Cross-level Feature Aggregation Network (CFA-Net) with Feature Enhancement Module (FEM) for multi-scale defect detection.

Designed a novel Collaborative Filtering Module (CFM) and a defect regression intersection over union (CDIou) loss function.

Main Results:

WTCF-Net demonstrated improved performance on NEU-DET, PCB, and DeepPCB datasets.
Achieved mAP increases of 5.9%, 1.3%, and 1.4% compared to baseline models.
The model operates at a detection speed of 53 FPS, indicating high efficiency.

Conclusions:

WTCF-Net effectively addresses challenges in industrial surface defect detection.
The proposed method shows significant improvements in accuracy and speed.
The model exhibits strong generalization capabilities and wide application potential in industrial inspection.