A multi-scale attention mechanism for detecting defects in leather fabrics

Hao Li ¹, Yifan Liu ¹, Huawei Xu ²

⁰School of Mathematics and Computer Science, Wuhan Polytechnic University, Wuhan, 430048, China.

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September 2, 2024

View abstract on PubMed

Summary

This study introduces a novel Multi-Layer Residual Convolutional Attention (MLRCA) approach for automated leather defect detection. The MLRCA method significantly improves the accuracy of identifying minor defects in industrial quality control.

Area Of Science

Industrial Engineering
Computer Vision
Materials Science

Background

Defect detection is crucial for quality control in leather production.
Challenges include diverse defect sizes, locations, and subtle inter-class variations.

Purpose Of The Study

To develop an advanced automated method for accurate leather defect detection.
To enhance the identification of small and similar defect types.

Main Methods

Proposed a Multi-Layer Residual Convolutional Attention (MLRCA) approach to improve feature representation.
Integrated MLRCA into a Feature Pyramid Network (FPN) creating ML-FPN for multi-scale fusion.
Implemented a Side-Aware Boundary Localization (SABL) detection head for precise defect positioning.

Main Results

The MLRCA-based model achieved high performance metrics: AP (83.4), AP50 (89.7), AP75 (85.6).
Excellent detection of small (AP_S: 71.3), medium (AP_M: 89.9), and large (AP_L: 88.9) defects.
Demonstrated superior capability in distinguishing between similar defect categories.

Conclusions

The proposed MLRCA and ML-FPN approach offers a feasible and effective solution for automated leather defect detection.
Provides new insights for enhancing industrial quality control in leather manufacturing.
The method shows significant potential for detecting subtle and minor defects.

Keywords:

Attention mechanism Convolutional Neural network Defect detection Multi-scale information