Surface Defect Detection Based on Adaptive Multi-Scale Feature Fusion
Surface defect detection plays a quality assurance role in industrial manufacturing processes. However, the diversity of defects and the presence of complex backgrounds bring significant challenges to salient object detection. To this end, this study proposes a new adaptive multi-scale feature fusio...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/25/6/1720 |
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| Summary: | Surface defect detection plays a quality assurance role in industrial manufacturing processes. However, the diversity of defects and the presence of complex backgrounds bring significant challenges to salient object detection. To this end, this study proposes a new adaptive multi-scale feature fusion network (AMSFF-Net) to solve the SOD problem of object surface defects. The upsampling fusion module used adaptive weight fusion, global feature adaptive fusion, and differential feature adaptive fusion to fuse information of different scales and levels. In addition, the spatial attention (SA) mechanism was introduced to enhance the effective fusion of multi-feature maps. Preprocessing techniques such as aspect ratio adjustment and random rotation were used. Aspect ratio adjustment helps to identify and locate defects of different shapes and sizes, and random rotation enhances the ability of the model to detect defects at different angles. The negative samples and non-uniform-distribution samples in the magnetic tile defect dataset were further removed to ensure data quality. This study conducted comprehensive experiments, demonstrating that AMSFF-Net outperforms existing state-of-the-art technologies. The proposed method achieved an S-measure of 0.9038 and an <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>F</mi></mrow><mrow><mi mathvariant="sans-serif">β</mi></mrow><mrow><mi mathvariant="normal">m</mi><mi mathvariant="normal">a</mi><mi mathvariant="normal">x</mi></mrow></msubsup></mrow></semantics></math></inline-formula> of 0.8782, which represents a 1% improvement in <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>F</mi></mrow><mrow><mi mathvariant="sans-serif">β</mi></mrow><mrow><mi mathvariant="normal">m</mi><mi mathvariant="normal">a</mi><mi mathvariant="normal">x</mi></mrow></msubsup></mrow></semantics></math></inline-formula> compared to the best existing methods. |
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| ISSN: | 1424-8220 |