Textured Surface Defect Detection with Image-Level Accuracy Optimization via Patch-Level Features and Simulation-Based Samples

Textured surface defect detection plays a critical role in industrial product quality monitoring. However, excessive reliance on pixel-level precision, especially when defect boundaries are unclear, can lead to more complex models, lower efficiency, and increased risk of false positives or missed de...

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Vydáno v:2025 4th Asia Conference on Algorithms, Computing and Machine Learning (CACML) s. 1 - 7
Hlavní autoři: Li, Fen, Li, Dongxiao, Huang, Le, Chen, Hangrui, Zhang, Ming
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 28.03.2025
Témata:
Accuracy
Computational efficiency
Computational modeling
Costs
Defect detection
Feature extraction
Labeling
Monitoring
Patch-Level Features
Robustness
Surface Defect Detection
Surface texture
Synthetic Defects
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Shrnutí:Textured surface defect detection plays a critical role in industrial product quality monitoring. However, excessive reliance on pixel-level precision, especially when defect boundaries are unclear, can lead to more complex models, lower efficiency, and increased risk of false positives or missed detections, ultimately compromising system reliability. Additionally, challenges such as the difficulty of collecting defect samples, severe class imbalances, and high pixel-level labeling costs hinder the effectiveness of convolutional neural networks in terms of both inference speed and computational efficiency. To overcome these challenges, we propose SiamSimNet, a dual-channel Siamese network that relies on high-quality training data generated through simulation algorithms, minimizing the need for real defect samples. By incorporating locally aware patch features, the proposed method significantly improves detection accuracy and robustness, while maintaining high inference efficiency. This approach shows strong potential for real-world industrial applications.
DOI:10.1109/CACML64929.2025.11010951