Abstract
Defect detection of fabrics is a necessary step for quality control in industries related to fabrics such as clothing and tents. Traditional fabric inspection relies on visual inspection, which is inefficient and inaccurate. The abnormal detection model based on PatchCore is based on the feature extraction of a pre-trained model on the general ImageNet large data set and performs well in industrial abnormal detection tasks. However, it is difficult to adapt to the noise problem of factory fabrics and scene adaptability. Therefore, in this paper, feature extraction is scenarized, and a small amount of real fabric data is used to fine-tune the pre-trained feature extraction network guided by object detection. This allows it to adapt to real industrial fabric abnormal detection scenes, and the scoring function is optimized to improve segmentation accuracy for noise problems. This solves the problem of insufficient fabric defect samples and the speed and accuracy requirements of defect detection in industrial scenes. The deployment and testing in the factory have effectively solved the problem of fabric detection in industrial scenes.
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Acknowledgments
This work was supported by the project “Research on Key AI Visual Technologies Based on Deep Learning and Their Industrial Application in Industrial Scenarios” of Quzhou Science and Technology Bureau.
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Wang, M., Wang, M., Liu, J., Niu, S., Zhang, W., Zhao, J. (2023). A Fabric Defect Detection Model Based on Feature Extraction of Weak Sample Scene. In: Tan, Y., Shi, Y., Luo, W. (eds) Advances in Swarm Intelligence. ICSI 2023. Lecture Notes in Computer Science, vol 13969. Springer, Cham. https://doi.org/10.1007/978-3-031-36625-3_20
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