Defect Detection Method Based on Hierarchical Microscopic Feature Modeling and Simulation

Authors

Jing Zou, School of Computer Science, Zhejiang University of Science and Technology, Hangzhou 310023, China
Xu Tan, School of Computer Science, Zhejiang University of Science and Technology, Hangzhou 310023, China; Zhejiang University, Hangzhou 310013, China
Junji Mao, School of Computer Science, Zhejiang University of Science and Technology, Hangzhou 310023, China
Haidong Gao, School of Computer Science, Zhejiang University of Technology, Hangzhou 310058, China
Jianrong Tan, Zhejiang University, Hangzhou 310013, China

Abstract

Abstract: To address the challenge of detecting small and low-contrast defects in complex microscopic images, a defect method technology based on hierarchical microscopic feature modeling and simulation is proposed. The method is built on the RT-DETR (real-time detection transformer)framework to construct the HM-RTDETR (hierarchical microscopic RT-DETR) model. It maintains the global feature modeling ability of the Transformer and introduces a Dense O2O-Mosaic, a high-density one-to-one Mosaic augmentation strategy, to increase supervision density for small samples. A depthwise separable convolution (DWConv) module is used to enhance local detail extraction in microscopic textures, and a learnable PatchExpand module is applied for spatial semantic reconstruction, improving the identification of tiny defects. Furthermore, the cooperative fusion of DWConv and PatchExpand achieves adaptive multi-scale feature integration and lightweight optimization. Experimental results show that the proposed model maintains consistent advantages under both offline augmentation settings: HM-RTDETR achieves mAP0.5-0.95 of 57.8% and 70.4% under Setting I and Setting II, respectively, improving over the corresponding baselines by 18.9% and 21.6%.The method achieves a good balance between accuracy and efficiency, providing an effective and extensible solution for automatic microscopic defect detection and simulation analysis.

Recommended Citation

Zou, Jing; Tan, Xu; Mao, Junji; Gao, Haidong; and Tan, Jianrong (2026) "Defect Detection Method Based on Hierarchical Microscopic Feature Modeling and Simulation," Journal of System Simulation: Vol. 38: Iss. 1, Article 1.
DOI: 10.16182/j.issn1004731x.joss.25-1166
Available at: https://dc-china-simulation.researchcommons.org/journal/vol38/iss1/1

First Page

1

Last Page

13

CLC

TP 391.9

Recommended Citation

Zou Jing, Tan Xu, Mao Junji, et al. Defect Detection Method Based on Hierarchical Microscopic Feature Modeling and Simulation[J]. Journal of System Simulation, 2026, 38(1): 1-13.

Corresponding Author

Tan Xu

DOI

10.16182/j.issn1004731x.joss.25-1166