Computer Science

Computer Science ›› 2025, Vol. 52 ›› Issue (11A): 241200124-6.doi: 10.11896/jsjkx.241200124

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

SAM-MR:SAM-based Mixed Region Matching Expert Adaptation Algorithm for FabricDetection

LUO Qifeng1, XIAO Xing1, WEN Chaofei1, CHI Mingmin2, PENG Bo3   

  1. 1 Zhongshan Power Supply Bureau,Guangdong Power Grid Co.,Ltd.,Zhongshan,Guangdong 528401,China
    2 School of Computer Science and Technology,Fudan University,Shanghai 200438,China
    3 College of Information,Shanghai Ocean University,Shanghai 201306,China
  • Online:2025-11-15 Published:2025-11-10
  • Supported by:
    Science and Technology Project of China Southern Power Grid Company(032000KC23030007(GDKJXM20230265)).

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Abstract: Supervised anomaly detection has been widely applied to fabric quality inspection due to its high precision in industrial scenarios.However,existing unified-architecture methods often suffer from limited feature adaptation capabilities,making it difficult to distinguish diverse and highly similar fabric defects.This paper proposes a novel approach based on a Mixture of Region Experts(SAM-MR),which introduces a Mixture of Adapter Experts module to differentiate between various types of fabric defects.Additionally,an Align and Differencing module is employed to align features between template and defect images,further enhancing the localization of anomalous regions.The model is also extended to incorporate component detection,enabling semantic recognition of defect-related components on top of defect localization.Experimental results demonstrate that SAM-MR outperforms existing methods on fabric defect datasets,and qualitative,quantitative,and ablation studies validate the effectiveness of the proposed approach in multi-task prediction.

Key words: Supervised learning, Anomaly detection, Mixture of experts

CLC Number: 

  • TP181
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