Journal of System Simulation
Abstract
Abstract: To address the issues of scarce and unknown types of abnormal defect data and the lack of diversity in anomaly representation in conventional knowledge distillation defect detection methods, a self-supervised distillation learning method based on discriminative enhancement is proposed. An attention-based multi-scale feature fusion module is proposed, which enhances the capability of anomaly representation by amplifying the multi-scale feature differences between the student network and the teacher network. A discriminative network composed of a feature reweighting module and a decoder is designed to generate more accurate anomaly score maps by further emphasizing the anomaly features in the teacher network, thereby improving the accuracy of the defect detection segmentation. Experiments show that the performance of the proposed method surpasses the existing knowledge distillation defect detection methods, verifying the effectiveness and superiority of the proposed method.
Recommended Citation
Feng, Zhiyuan and Chen, Ying
(2025)
"Self-supervised Defect Detection via Discriminative Enhancement-based Distillation Learning,"
Journal of System Simulation: Vol. 37:
Iss.
6, Article 14.
DOI: 10.16182/j.issn1004731x.joss.24-0206
Available at:
https://dc-china-simulation.researchcommons.org/journal/vol37/iss6/14
First Page
1499
Last Page
1511
CLC
TP391.4
Recommended Citation
Feng Zhiyuan, Chen Ying. Self-supervised Defect Detection via Discriminative Enhancement-based Distillation Learning[J]. Journal of System Simulation, 2025, 37(6): 1499-1511.
DOI
10.16182/j.issn1004731x.joss.24-0206
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