Self-supervised Defect Detection via Discriminative Enhancement-based Distillation Learning

doi:10.16182/j.issn1004731x.joss.24-0206

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.

Key words: defect detection, knowledge distillation, attention-based multi-scale fusion module, feature reweighting module, discriminative network

CLC Number:

TP391.4

Feng Zhiyuan, Chen Ying. Self-supervised Defect Detection via Discriminative Enhancement-based Distillation Learning[J]. Journal of System Simulation, 2025, 37(6): 1499-1511.

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References 25

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	类别	US	MKD	PaDiM	EdgRec	DRAEM	RD	DRAD	STPM	本文
纹理类	地毯	91.6	79.3	99.8	97.4	97.0	98.9	99.3	—	99.8
	网格	81.0	78.0	96.7	99.7	99.9	100	99.9	—	100
	皮革	88.2	95.1	100	100	100	100	99.9	—	100
	瓷砖	99.1	91.6	98.1	100	99.6	99.3	99.2	—	100
	木材	97.7	94.3	99.2	94.0	99.1	99.2	99.6	—	99.7
对象类	瓶底	99.0	99.4	99.9	100	99.2	100	100	—	100
	电缆	86.2	89.2	92.7	97.9	91.8	95.0	98.6	—	98.2
	胶囊	86.1	80.5	91.3	95.5	98.5	96.3	98.1	—	97.6
	榛子	93.1	98.4	92.0	98.4	100	99.9	100	—	100
	齿轮	82.0	73.6	98.7	97.3	98.7	100	99.6	—	100
	药片	87.9	82.7	93.3	99.0	98.9	96.6	96.8	—	99.1
	螺钉	54.9	83.3	85.8	89.9	93.9	97.0	97.6	—	98.4
	牙刷	95.3	92.2	96.1	100	100	99.5	97.2	—	99.4
	晶体管	81.8	85.6	97.4	99.8	93.1	96.7	98.9	—	98.2
	拉链	91.9	93.2	90.3	98.3	100	98.5	98.8	—	98.1
	平均值	87.7	87.8	95.5	97.8	98.0	98.5	98.9	95.5	99.2

类别		US	MKD	EdgRec	DRAEM	PaDiM	DRAD	RD	STPM	本文
纹理类	地毯	—/87.9	95.6/—	99.4/96.9	95.5/—	99.1/96.2	98.7/—	98.9/97.0	98.8/95.8	99.5/98.0
	网格	—/95.2	91.8/—	99.2/97.0	99.7/—	97.3/94.6	98.9/—	99.3/97.6	99.0/96.6	99.2/96.9
	皮革	—/94.5	98.1/—	99.7/98.8	98.6/—	99.2/97.8	99.1/—	99.4/99.1	99.3/98.0	99.8/99.4
	瓷砖	—/94.6	82.8/—	98.6/96.3	99.2/—	94.1/86.0	95.2/—	95.6/90.6	97.4/92.1	98.4/96.1
	木材	—/91.1	84.8/—	91.4/77.5	96.4/—	94.9/91.1	94.9/—	95.3/90.9	97.2/93.6	97.2/94.7
对象类	瓶底	—/93.1	96.3/—	98.3/94.3	99.1/—	98.3/94.8	98.6/—	98.7/96.6	98.8/95.1	99.3/97.4
	电缆	—/81.8	82.4/—	97.7/88.7	94.7/—	96.7/88.8	96.9/—	97.4/91.0	95.5/87.7	96.6/90.5
	胶囊	—/96.8	95.9/—	95.2/82.2	94.3/—	98.5/93.5	98.9/—	98.7/95.8	98.3/92.2	98.7/95.8
	榛子	—/96.5	94.6/—	99.4/95.4	99.7/—	98.2/92.6	98.9/—	98.9/95.5	98.5/94.3	99.4/96.5
	齿轮	—/94.2	86.4/—	98.0/91.2	99.5/—	97.2/85.6	98.5/—	97.3/92.3	97.6/94.5	97.6/92.8
	药片	—/96.1	89.6/—	98.7/96.1	97.6/—	95.7/82.7	99.1/—	98.2/96.4	97.8/96.5	99.3/97.9
	螺钉	—/94.2	96.0/—	97.7/89.3	97.6/—	98.5/94.4	99.2/—	99.6/98.2	98.3/93.0	99.6/98.2
	牙刷	—/93.3	96.1/—	99.2/94.9	98.1/—	98.8/93.1	98.8/—	99.1/94.5	98.9/92.2	99.0/93.7
	晶体管	—/66.6	76.5/—	94.3/87.7	90.9/—	97.5/84.5	91.4/—	92.5/78.0	82.5/69.5	92.5/73.5
	拉链	—/95.1	93.9/—	98.7/96.0	98.8/—	98.5/95.9	98.5/—	98.2/95.4	98.5/95.2	99.0/95.9
	平均值	—/91.4	90.7/—	97.7/92.1	97.3/—	97.5/92.1	97.7/—	97.8/93.9	97.0/92.1	98.3/94.5

新指标	ResNet18	ResNet50	WideResNet50
DET-AUROC	98.1	98.6	99.2
SET-AUROC	97.6	98.0	98.3
PRO-AUROC	93.1	93.9	94.5

师生网络	判别网络	AMFF	DET-AUROC	SET-AUROC	PRO-AUROC
√			95.0	97.1	92.3
√	√		98.7	98.1	94.1
√		√	98.2	97.7	93.9
√	√	√	99.2	98.3	94.5

方法	DET-AUROC	SEG-AUROC	PRO-AUROC
师生网络	95.0	97.1	92.3
MFF+师生网络	97.4	97.3	92.8
AMFF+师生网络	98.2	97.7	93.9